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This study aimed to develop and evaluate a deep learning-based model that could automatically measure anterior segment (AS) parameters on preoperative ultrasound biomicroscopy (UBM) images of implantable Collamer lens (ICL) surgery candidates. A total of 1164 panoramic UBM images were preoperatively obtained from 321 patients who received ICL surgery in the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. First, the UNet++ network was utilized to segment AS tissues automatically, such as corneal lens and iris. In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks (ALs) of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA), and sulcus-to-sulcus distance (STS). Based on the results of the latter two processes, PD, ACD, ATA, and STS can be measured. Meanwhile, an external dataset of 294 images from Huangshi Aier Eye Hospital was employed to further assess the model's performance in other center. Lastly, a subset of 100 random images from the external test set was chosen to compare the performance of the model with senior experts. Whether in the internal test dataset or external test dataset, using manual labeling as the reference standard, the models achieved a mean Dice coefficient exceeding 0.880. Additionally, the intra-class correlation coefficients (ICCs) of ALs' coordinates were all greater than 0.947, and the percentage of Euclidean distance distribution of ALs within 250 μm was over 95.24%.While the ICCs for PD, ACD, ATA, and STS were greater than 0.957, furthermore, the average relative error (ARE) of PD, ACD, ATA, and STS were below 2.41%. In terms of human versus machine performance, the ICCs between the measurements performed by the model and those by senior experts were all greater than 0.931. A deep learning-based model could measure AS parameters using UBM images of ICL candidates, and exhibited a performance similar to that of a senior ophthalmologist. [ABSTRACT FROM AUTHOR]

The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images. A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts. The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001). The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma. [ABSTRACT FROM AUTHOR]

Sulfate concentrations in eutrophic waters continue to increase; however, the transformations of arsenic (As) in sediments under these conditions are unclear. In this study, we constructed a series of microcosms to investigate the effect of algal degradation on As transformations in sediments with high sulfate concentrations. The results showed that both the elevated sulfate levels and algal degradation enhanced the release of As from sediments to the overlying water, and degradation of algal in the presence of elevated sulfate levels could further contribute to As release. Sulfate competed with arsenate for adsorption in the sediments, leading to As desorption, while algal degradation created a strongly anaerobic environment, leading to the loss of the redox layer in the surface sediments. With high sulfate, algal degradation enhanced sulfate reduction, and sulfur caused the formation of thioarsenates, which may cause re-dissolution of the arsenides, enhancing As mobility by changing the As speciation. The results of sedimentary As speciation analysis indicated that elevated sulfur levels and algal degradation led to a shift of As from Fe 2 O 3 /oxyhydroxide-bound state to specifically adsorbed state at the sediment water interface. This study indicated that algal degradation increases the risk of As pollution in sulfate-enriched eutrophic waters. [Display omitted] • Elevated sulfate in water promoted sedimentary As release under oxic condition. • Sulfate reduction and iron(Fe) speciation transformation together control As release. • Elevated sulfate and algal degradation reduced Fe 2 O 3 /oxyhydroxides-bound As. • Algal degradation under high sulfate enhanced the As release from sediments. [ABSTRACT FROM AUTHOR]

• Retail investor's online searching on the dissemination of corporate green knowledge is explored in the Chinese market. • Investor's searching has a significant positive impact on the corporate green information dissemination. • Online media coverage and social media posts could effectively moderate this relationship and improve green knowledge sharing. The dissemination of green knowledge can effectively promote knowledge sharing and facilitate green technology innovation, which is important for mitigating climate risk at the microenterprise level. This study focuses on the effect of retail investors' internet information search behavior on the dissemination of corporate green knowledge. Using the investors' online searching volume and green patent citations of Chinese publicly listed companies from 2011 to 2020, we find that retail investors' online searching has a significant positive impact on the green information dissemination of firms. Online media coverage and social media posts could effectively moderate this relationship and improve green knowledge sharing. [ABSTRACT FROM AUTHOR]

During groundwater recharge with reclaimed water, pentabromodiphenyl ether (BDE-99) might contaminate the underground aquifers. In order to understand the effects of elevated sulfate concentrations on the biodegradation of BDE-99, lab-scale column experiments were performed to study the changes in bacterial and archaeal communities. The results confirmed that the biodegradation rate of BDE-99 improved significantly with the addition of sulfate. Oxygen was replaced by sulfate as the predominant electron acceptor, and the major biodebromination metabolites of BDE-99 were BDE-28 and BDE-47, respectively, The results from 454 pyrosequencing of 16s rRNA genes showed that the microbial community structure shifted toward communities with BDE-99 co-metabolism ability. At the bacterial genus level, Geothrix , Thiobacillus , Sphingomonas , Paenibacillus and Desulforhabdus were the five most abundant genera, wherein, Thiobacillus was able to treat the toxic products generated during polybrominated diphenyl ethers (PBDE) biodegradation. In the case of archaea at the genus level, Methanobrevibacter , Methanomethylovorans , DHVEG-6, Candidatus Nitrososphaera and Methanosphaera were the predominant groups, of which Methanobrevibacter and Methanosphaera showed good ability to metabolize BDE-99 with sulfate amendment. [ABSTRACT FROM AUTHOR]

The use of tertiary-treated municipal wastewater for groundwater recharge through river utilization may cause pentabromodiphenyl ether (BDE-99) pollution, which poses a potential risk to groundwater aquifers. Elevated nitrate levels may change the structure of microbial communities, thus influencing the biodegradation of BDE-99. To investigate the influence of elevated nitrate levels on the changes in microbial communities and associated biodegradation of BDE-99 appropriately, a laboratory-scale column was designed to simulate the percolation process. The results showed that the attenuation rate of BDE-99 increased with nitrate concentrations. The lower-brominated ether congeners were the primary debrominated metabolites, while high-brominated diphenyl ethers and isomers of BDE-100 also emerged, and their concentrations decreased with reduced nitrate concentrations. The microbial community composition and structure significantly changed with elevated nitrate levels and relative abundance of nitrate-reducing bacteria with diverse metabolic capabilities increased. The order of the relative abundance of the largest five classes, as well as the relative abundances of the largest five families and genera, changed with increasing nitrate concentrations. The diversity of microorganisms with universal aromatics-degrading abilities related to nitrogen transformation played an important role in mitigating the infiltration of BDE-99 to groundwater. [ABSTRACT FROM AUTHOR]

Pentabromodiphenyl ether (BDE-99) pollution can affect groundwater that has been recharged with treated municipal wastewater. To properly investigate the effect of biochar on the anaerobic degradation of BDE-99 by an archaeal community, we established three lab-scale percolation columns to simulate the recharge process. BDE-99 content decreased with increasing soil depth, especially in the biochar-amended column (BCA), which demonstrated high adsorption capacity and biodegradability. The essential biodegradation of BDE-99 by archaea was confirmed during the recharge process by fluorescence in situ hybridization (FISH) analysis. The effect of biochar on the vertical distribution of typical archaea taxa associated with BDE-99 degradation in the river-based aquifer media was confirmed by 454 pyrosequencing the archaeal 16S ribosomal RNA (16S rRNA) genes. As well as affecting the concentration of polybrominated diphenyl ethers, biochar improved the biodiversity of the archaea and had a critical influence on the majority of archaeal communities. We observed large populations of Nitrosopumilus , Nitrososphaera , and Methanomethylovorans , which are associated with the anaerobic degradation of BDE-99, and the populations of some novel archaea genera, such as Halorhabdus and Methanospirillum , increased markedly owing to the effect of biochar. We suggest that biochar could play a positive role in the anaerobic degradation of BDE-99, and could provide a cost-effective means of mitigating the migration of pollutants. [ABSTRACT FROM AUTHOR]

When using treated municipal wastewater for recharging river-based natural groundwater, pentabromodiphenyl ether (BDE-99) pollution might be a potential threat to underground aquifers. Due to their strong affinity for black carbon (BC), the migration and bioavailability of PBDEs may be decreased in aquifer media with a high BC content. To properly examine the effect of black carbon on the migration and biodegradation of BDE-99, a lab-scale column was set up to simulate the recharge process. The filler media of the two columns mainly comprised silty clay (SC) and black carbon amendment silty clay (BCA). The results showed that the attenuation effect of BDE-99 was in the order of BCA > SC. The attenuation rate constants of BED-99 in the SC and BCA systems were 0.12 and 0.18 m − 1 , respectively, which followed first-order kinetics. The two turning points of the BDE-99 percentage with depth in the leachate were 0.15 m and 0.45 m due to fast sorption and biodegradation, respectively. The primary debrominated metabolites were not the only lower brominated ether congeners; high-brominated diphenyl ethers and isomers of BDE-99 were also produced during the biotransformation process. The accumulation of BDE-99 and PBDEs in the soil was higher in BCA soils than in the SC system. The bacterial community in the upper layer was more diverse than in the bottom layer. Five known bacterial classes ( Gammaproteobacteria, Alphaproteobacteria, Betaproteobacteria, Acidobacteria and Clostridia ) and seventeen known bacterial genera (three major genera: Halomonas, Pseudomonas and Shewanella ) were considered to be PBDE-degradation-associated bacteria. The bacterial community diversity and percentage of PBDE-degradation-associated bacteria in the BCA system were marginally greater than those in the SC system. The higher bacterial diversity and adsorption capacity in the BCA system were able mitigate the migration of BDE-99 into groundwater. [ABSTRACT FROM AUTHOR]

Abstract: Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most economically important pathogens for swine industry. The non-structural protein 2 (Nsp2) is considered to be one of the immunogenic proteins of PRRSV. In this study, the B-cell epitopes of the Nsp2 protein of a North American type Chinese strain PRRSV BJ-4 were identified on a prokaryotic expressed Nsp2 fragment (73–567aa). A total of six monoclonal antibodies (mAbs) recognizing different epitopes on the expressed protein were prepared. All six mAbs exhibited immunoreactivity with the denatured Nsp2 protein in Western blotting and produced strong perinuclear staining in PRRSV infected MARC-145 cells in an immunofluorescence assay. Pepscan analysis revealed six distinct linear epitopes for the six mAbs, respectively, and of which four were identified to be novel linear Nsp2 B-cell epitopes: T73LPERVRPPDDWAT86, D385ELKDQMEED394, P452VPAPRRKVGSDCGS466, and P467VSLGGDVPNS477. All of the six mAb specific peptides could be recognized by porcine PRRSV antiserum, indicating that the epitopes involving these synthetic peptides were immunogenic and immunodominant during PRRSV infection in pigs. Our results provided valuable information for developing novel PRRSV vaccines using the Nsp2 epitopes as potential serological markers. [Copyright &y& Elsevier]

The low-carbon economic operation of integrated energy systems (IES) cannot be separated from the carbon trading and green certificate trading market. Therefore, a low-carbon market mechanism and optimization method for IES is proposed. First, we establish a "green-carbon" offset mechanism to realize the conversion from tradable green certificate (TGC) to carbon quotas to offset system's carbon emissions. Second, considering the impact of market incentives and users' consumption behavior on IES, an energy management method of IES is put forward based on Stackelberg game. IES operators as leaders decide energy prices and trading strategies of carbon, TGC and multi-energy. Energy users as followers participate in the integrated demand response based on energy price. The game is solved by an improved adaptive catastrophic genetic algorithm and CPLEX solver. Finally, we take an industrial park in China as an example to analyze. The results show that the proposed method can significantly reduce the system's carbon emissions while improving the benefits for both IES and consumers. • A mechanism is proposed to offset system carbon emissions with green certificates. • An intelligent pricing and optimal method is proposed based on Stackelberg game. • Carbon capture and power-to-gas technology are used to reduce carbon emissions. • Key parameters sensitivity of the offset mechanism is analyzed for specific cases. [ABSTRACT FROM AUTHOR]

In this study, a hybrid photovoltaic-wind-concentrated solar power renewable energy system and two cogeneration models are proposed. Evaluation criteria are employed, including the levelized cost of energy (LCOE) and the loss of power supply probability (LPSP). The optimal configuration and dynamic dispatch strategy of the hybrid system are determined through multi-objective optimization using the natural resources of Xining City. The economic feasibility of the system is assessed through comparison and analysis. The findings reveal that both cogeneration modes of the system effectively meet the power output requirements and dynamic thermal load. In thermal energy storage tanks' heat production mode without a battery storage system, the system achieves a minimum LCOE of 0.0526$/kWh and a maximum LPSP of 6.86%. With a battery storage system in this mode, the system attains a minimum LPSP of 4.83% and a maximum LCOE of 0.160$/kWh. Additionally, in the turbine spent vapor recovery heat supply mode, the system exhibits an LCOE of 0.127$/kWh and an LPSP of 4.96%, the system has better flexibility and wind-solar complementarity in this mode. This study also analyzed the importance of affordable electric energy storage for stable energy supply in hybrid energy systems. [ABSTRACT FROM AUTHOR]

Recent remote-sensing-based global carbon, water and energy budgets over land still include considerable uncertainties. Most existing flux products of terrestrial carbon, water and energy components were developed individually, despite the inherently coupled processes among them. In this study, we present a new set of global daily surface downwelling shortwave radiation (SW), net radiation (R net), evapotranspiration (ET), gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) datasets at 0.05° resolutions from 1982 to 2019, by improving a satellite-based and coupled-process model—the Breathing Earth System Simulator (BESS). The new version of BESS (v2.0) integrated a newly developed ecosystem respiration module, an optimality-based maximum carboxylation rate (V cmax) model, and extended the temporal coverage of flux datasets from 1982 to 2019. We evaluated BESS products against the FLUXNET2015 dataset at the site scale, and against several remote sensing and/or machine learning products on a global scale. At the site scale, BESS products agreed well with FLUXNET measurements, capturing 84%, 53%, 65%, 51% and 31% of daily variation in R net , ET, GPP, TER and NEE, respectively. Interannual variation in BESS NEE showed relatively low consistency with the FLUXNET measurements, while the rest fluxes explained approximately half of the interannual variation. On a global scale, we found marked discrepancies in spatio-temporal patterns between BESS and several benchmark products. Over the period 1982–2019, BESSv2.0 estimated the mean annual global R net , ET, GPP, TER and NEE to be 340.97 ± 5.22 ZJ yr−1 (mean ± 1 SD), 67.67 ± 0.71 103 km3 yr−1, 125.74 ± 5.95 Pg C yr−1, 109.30 ± 3.16 Pg C yr−1, and − 16.28 ± 2.95 Pg C yr−1, respectively, with significant annual linear trends (P < 0.01) by −0.05 103 km3 yr−2 for ET, by 0.52 Pg C yr−2 for GPP, by 0.28 Pg C yr−2 for TER, and by −0.25 Pg C yr−2 for NEE. We further evaluated various coupled processes derived by BESS in terms of functional properties (i.e., Budyko relation, carbon-use efficiency, water-use efficiency, and light-use efficiency), which agreed well with FLUXNET observations, unlike the benchmark products. Overall, BESSv2.0 can serve as a set of reliable and independent products from other global satellite products, facilitating studies related to global carbon, water and energy budgets in a coupled and comprehensive manner. • Generated a set of long-term radiation, energy and carbon flux products using BESSv2.0. • Agreed with FLUXNET and benchmark datasets. • Revealed functional relationships between water and carbon fluxes. [ABSTRACT FROM AUTHOR]

In present report, a new BDA for Cu 2+ has been disclosed for the first time. The presence of Cu 2+ caused an obvious fluorescence enhancement at 407 nm upon excitation at 360 nm, which could be distinguished by the naked eye under a UV lamp. It was found to have acceptable detection limits (0.12 μM), high selectivity and reversibility/reusability. The photoluminescence measurement indicates that BDA with Cu 2+ presence, showed very strong blue emission bands with CIE chromaticity coordinates (0.1652, 0.0723). Besides, the designed probe has shown potential applications in the area of sample monitoring, photo-printing. [ABSTRACT FROM AUTHOR]

When using reclaimed water for the river-based artificial groundwater recharge, endocrine disrupting chemicals (EDCs) pollution is believed to be a potential threat to the drinking water supplies in Beijing, China. Lab-scale column experiments simulating recharge were conducted to study the migration and transformation of three selected EDCs, namely ethinylestradiol (EE2), 4-n-Nonylphenol (NP) and bisphenol A (BPA). The filler media of the two columns mainly consisted of fine sand (FS) in the middle section and less-permeable silty clay (SC) in the southern section, all representative soil vadose zone media from Chaobai river in Beijing. The results showed that the attenuation effect of EDCs was in the order of NP > EE2 > BPA and all of them followed a first-order kinetics, while the migration capacity was in the reverse order. The residual EDCs in different depths of SC were much higher than those in FS, suggesting that the SC soil has a significantly higher adsorption capacity. The attenuation effect of NP and BPA by unit mass was better in FS than that in SC, indicating that the biodegradation was the main attenuation mechanism. The bacterial biomass and community diversity decreased with depth in SC, while the changes in FS were unobvious. According to the DGGE fingerprints, the bacterial community in the middle layer was more diverse than in the upper layer, which was related to the EDCs concentrations and transformation products in the water-soil system. The dominant group was found to be proteobacteria, including Alphaproteobacteria, Betaproteobacteria and Gammaproteobacteria, suggesting that these microbes might play an important role in EDC transformation. The described process provides a potential pathway for improving the removal of EDCs. [ABSTRACT FROM AUTHOR]