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Tian Y, Yang X, Chen N, Li C, Yang W. Data-driven interpretable analysis for polysaccharide yield prediction. Environ Sci Ecotechnol 2024; 19:100321. [PMID: 38021368 PMCID: PMC10661693 DOI: 10.1016/j.ese.2023.100321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 09/17/2023] [Accepted: 09/17/2023] [Indexed: 12/01/2023]
Abstract
Cornstalks show promise as a raw material for polysaccharide production through xylanase. Rapid and accurate prediction of polysaccharide yield can facilitate process optimization, eliminating the need for extensive experimentation in actual production to refine reaction conditions, thereby saving time and costs. However, the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately. Here, we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production. We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations. Notably, Random Forest (RF) and eXtreme Gradient Boost (XGB) demonstrate robust performance, achieving prediction accuracies of 93.0% and 95.6%, respectively, while an independently developed deep neural network (DNN) model achieves 91.1% accuracy. A feature importance analysis of XGB reveals the enzyme solution volume's dominant role (43.7%), followed by time (20.7%), substrate concentration (15%), temperature (15%), and pH (5.6%). Further interpretability analysis unveils complex parameter interactions and potential optimization strategies. This data-driven approach, incorporating machine learning, deep learning, and interpretable analysis, offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues.
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Affiliation(s)
- Yushi Tian
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Xu Yang
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Nianhua Chen
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Chunyan Li
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Wulin Yang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, PR China
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2
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Chen S, Xiao Y, Zhang C, Lu X, He K, Hao J. Cost dynamics of onshore wind energy in the context of China's carbon neutrality target. Environ Sci Ecotechnol 2024; 19:100323. [PMID: 38021369 PMCID: PMC10654034 DOI: 10.1016/j.ese.2023.100323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 09/24/2023] [Accepted: 09/24/2023] [Indexed: 12/01/2023]
Abstract
Wind energy has become one of the most important measures for China to achieve its carbon neutrality goal. The spatial and temporal evolvement of economic competitiveness for wind energy becomes an important concern in shaping the decarbonization pathway in China. There has been an urgent need in power system planning to model the future dynamics of cost decline and supply potential for wind power in the context of carbon neutrality until 2060. Existing studies often fail to capture the rapid decline in the cost of wind power generation in recent years, and the prediction of wind power cost decline is more conservative than the reality. This study constructs an integrated model to evaluate the cost-competitiveness and grid parity potential of China's onshore wind electricity at fine spatial resolution with updated parameters. Results indicate that the total onshore wind potential amounts to 54.0 PWh. The average levelized cost of wind power is expected to decline from CNY 0.39 kWh-1 in 2020 to CNY 0.30 and CNY 0.21 kWh-1 in 2030 and 2060. 28.3%, 67.6%, and 97.6% of the technical potentials hold power costs lower than coal power in 2020, 2030, and 2060.
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Affiliation(s)
- Shi Chen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
| | - Youxuan Xiao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
| | - Chongyu Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
| | - Xi Lu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Kebin He
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
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Lei Y, Yin Z, Lu X, Zhang Q, Gong J, Cai B, Cai C, Chai Q, Chen H, Chen R, Chen S, Chen W, Cheng J, Chi X, Dai H, Feng X, Geng G, Hu J, Hu S, Huang C, Li T, Li W, Li X, Liu J, Liu X, Liu Z, Ma J, Qin Y, Tong D, Wang X, Wang X, Wu R, Xiao Q, Xie Y, Xu X, Xue T, Yu H, Zhang D, Zhang N, Zhang S, Zhang S, Zhang X, Zhang X, Zhang Z, Zheng B, Zheng Y, Zhou J, Zhu T, Wang J, He K. The 2022 report of synergetic roadmap on carbon neutrality and clean air for China: Accelerating transition in key sectors. Environ Sci Ecotechnol 2024; 19:100335. [PMID: 37965046 PMCID: PMC10641488 DOI: 10.1016/j.ese.2023.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/16/2023]
Abstract
China is now confronting the intertwined challenges of air pollution and climate change. Given the high synergies between air pollution abatement and climate change mitigation, the Chinese government is actively promoting synergetic control of these two issues. The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators. The Synergetic Roadmap 2022 report is the first annual update, featuring 20 indicators across five aspects: synergetic governance system and practices, progress in structural transition, air pollution and associated weather-climate interactions, sources, sinks, and mitigation pathway of atmospheric composition, and health impacts and benefits of coordinated control. Compared to the comprehensive review presented in the 2021 report, the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones. These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time, a decline in the production of crude steel and cement after years of growth, and the surging penetration of electric vehicles. Additionally, in 2022, China issued the first national policy that synergizes abatements of pollution and carbon emissions, marking a new era for China's pollution-carbon co-control. These changes highlight China's efforts to reshape its energy, economic, and transportation structures to meet the demand for synergetic control and sustainable development. Consequently, the country has witnessed a slowdown in carbon emission growth, improved air quality, and increased health benefits in recent years.
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Affiliation(s)
- Yu Lei
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Zhicong Yin
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xi Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jicheng Gong
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Cilan Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Qimin Chai
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Huopo Chen
- Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, 200032, China
| | - Shi Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Wenhui Chen
- School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Jing Cheng
- Department of Earth System Science, University of California, Irvine, CA, 92697, USA
| | - Xiyuan Chi
- National Meteorological Center, China Meteorological Administration, Beijing, 100081, China
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Xiangzhao Feng
- Policy Research Center for Environment and Economy, Ministry of Ecology and Environment of the People's Republic of China, Beijing, 100029, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Shan Hu
- Building Energy Research Center, School of Architecture, Tsinghua University, Beijing, 100084, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, 100084, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Wei Li
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xiaomei Li
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Jun Liu
- Department of Environmental Engineering, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xin Liu
- Energy Foundation China, Beijing, 100004, China
| | - Zhu Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jinghui Ma
- Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai, 200030, China
| | - Yue Qin
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Dan Tong
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xuhui Wang
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Xuying Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Rui Wu
- Transport Planning and Research Institute (TPRI) of the Ministry of Transport, Beijing, 100028, China
| | - Qingyang Xiao
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Xiaolong Xu
- China Association of Building Energy Efficiency, Beijing, 100029, China
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100080, China
| | - Haipeng Yu
- Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Da Zhang
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Ning Zhang
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Xian Zhang
- The Administrative Centre for China's Agenda 21 (ACCA21), Ministry of Science and Technology (MOST), Beijing, 100038, China
| | - Xin Zhang
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Zengkai Zhang
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Xiamen University, Xiamen, 361102, China
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Jian Zhou
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Tong Zhu
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Jinnan Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
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Xu B, Yu H, Shi Z, Liu J, Wei Y, Zhang Z, Huangfu Y, Xu H, Li Y, Zhang L, Feng Y, Shi G. Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate. Environ Sci Ecotechnol 2024; 19:100333. [PMID: 38021366 PMCID: PMC10661687 DOI: 10.1016/j.ese.2023.100333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 12/01/2023]
Abstract
Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO3-)). The mechanism between ε(NO3-) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3-). Here we introduce a supervised machine learning approach-the multilevel nested random forest guided by theory approaches. Our approach robustly identifies NH4+, SO42-, and temperature as pivotal drivers for ε(NO3-). Notably, substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results. Furthermore, our approach underscores the significance of NH4+ during both daytime (30%) and nighttime (40%) periods, while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis. This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.
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Affiliation(s)
- Bo Xu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Haofei Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Zongbo Shi
- School of Geography Earth and Environment Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Jinxing Liu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin Key Laboratory of air Pollutants Monitoring Technology, School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin, 300072, China
- Gigantic Technology (Tianjin) Co., Ltd, Tianjin, 300072, China
| | - Yuting Wei
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Zhongcheng Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Yanqi Huangfu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Han Xu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Yue Li
- College of Computer Science, Nankai University, Tianjin, 300350, China
| | - Linlin Zhang
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Guoliang Shi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
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Ma JX, Wang X, Pan YR, Wang ZY, Guo X, Liu J, Ren NQ, Butler D. Data-driven systematic analysis of waterborne viruses and health risks during the wastewater reclamation process. Environ Sci Ecotechnol 2024; 19:100328. [PMID: 37965045 PMCID: PMC10641159 DOI: 10.1016/j.ese.2023.100328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 09/27/2023] [Accepted: 09/27/2023] [Indexed: 11/16/2023]
Abstract
Waterborne viral epidemics are a major threat to public health. Increasing interest in wastewater reclamation highlights the importance of understanding the health risks associated with potential microbial hazards, particularly for reused water in direct contact with humans. This study focused on identifying viral epidemic patterns in municipal wastewater reused for recreational applications based on long-term, spatially explicit global literature data during 2000-2021, and modelled human health risks from multiple exposure pathways using a well-established quantitative microbial risk assessment methodology. Global median viral loads in municipal wastewater ranged from 7.92 × 104 to 1.4 × 106 GC L-1 in the following ascending order: human adenovirus (HAdV), norovirus (NoV) GII, enterovirus (EV), NoV GI, rotavirus (RV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Following secondary or tertiary wastewater treatment, NoV GI, NoV GII, EV, and RV showed a relatively higher and more stable log reduction value with medians all above 0.8 (84%), whereas SARS-CoV-2 and HAdV showed a relatively lower reduction, with medians ranging from 0.33 (53%) to 0.55 (72%). A subsequent disinfection process effectively enhanced viral removal to over 0.89-log (87%). The predicted event probability of virus-related gastrointestinal illness and acute febrile respiratory illnesses in reclaimed recreational water exceeded the World Health Organization recommended recreational risk benchmark (5% and 1.9%, respectively). Overall, our results provided insights on health risks associated with reusing wastewater for recreational purposes and highlighted the need for establishing a regulatory framework ensuring the safety management of reclaimed waters.
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Affiliation(s)
- Jia-Xin Ma
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xu Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China
- Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, United Kingdom
| | - Yi-Rong Pan
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhao-Yue Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China
| | - Xuesong Guo
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
| | - Junxin Liu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
| | - Nan-Qi Ren
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China
| | - David Butler
- Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, United Kingdom
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Bian Y, Leininger A, May HD, Ren ZJ. H 2 mediated mixed culture microbial electrosynthesis for high titer acetate production from CO 2. Environ Sci Ecotechnol 2024; 19:100324. [PMID: 37961049 PMCID: PMC10637882 DOI: 10.1016/j.ese.2023.100324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 11/15/2023]
Abstract
Microbial electrosynthesis (MES) converts CO2 into value-added products such as volatile fatty acids (VFAs) with minimal energy use, but low production titer has limited scale-up and commercialization. Mediated electron transfer via H2 on the MES cathode has shown a higher conversion rate than the direct biofilm-based approach, as it is tunable via cathode potential control and accelerates electrosynthesis from CO2. Here we report high acetate titers can be achieved via improved in situ H2 supply by nickel foam decorated carbon felt cathode in mixed community MES systems. Acetate concentration of 12.5 g L-1 was observed in 14 days with nickel-carbon cathode at a poised potential of -0.89 V (vs. standard hydrogen electrode, SHE), which was much higher than cathodes using stainless steel (5.2 g L-1) or carbon felt alone (1.7 g L-1) with the same projected surface area. A higher acetate concentration of 16.0 g L-1 in the cathode was achieved over long-term operation for 32 days, but crossover was observed in batch operation, as additional acetate (5.8 g L-1) was also found in the abiotic anode chamber. We observed the low Faradaic efficiencies in acetate production, attributed to partial H2 utilization for electrosynthesis. The selective acetate production with high titer demonstrated in this study shows the H2-mediated electron transfer with common cathode materials carries good promise in MES development.
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Affiliation(s)
- Yanhong Bian
- Department of Civil and Environmental Engineering, Princeton University, 86 Olden St, Princeton, NJ, 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, 86 Olden St., Princeton, NJ, 08544, United States
| | - Aaron Leininger
- Department of Civil and Environmental Engineering, Princeton University, 86 Olden St, Princeton, NJ, 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, 86 Olden St., Princeton, NJ, 08544, United States
| | - Harold D. May
- Andlinger Center for Energy and the Environment, Princeton University, 86 Olden St., Princeton, NJ, 08544, United States
| | - Zhiyong Jason Ren
- Department of Civil and Environmental Engineering, Princeton University, 86 Olden St, Princeton, NJ, 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, 86 Olden St., Princeton, NJ, 08544, United States
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7
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Ra K, Proctor C, Ley C, Angert D, Noh Y, Odimayomi T, Whelton AJ. Four buildings and a flush: Lessons from degraded water quality and recommendations on building water management. Environ Sci Ecotechnol 2024; 18:100314. [PMID: 37854462 PMCID: PMC10579424 DOI: 10.1016/j.ese.2023.100314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 10/20/2023]
Abstract
A reduction in building occupancy can lead to stagnant water in plumbing, and the potential consequences for water quality have gained increasing attention. To investigate this, a study was conducted during the COVID-19 pandemic, focusing on water quality in four institutional buildings. Two of these buildings were old (>58 years) and large (>19,000 m2), while the other two were new (>13 years) and small (<11,000 m2). The study revealed significant decreases in water usage in the small buildings, whereas usage remained unchanged in the large buildings. Initial analysis found that residual chlorine was rarely detectable in cold/drinking water samples. Furthermore, the pH, dissolved oxygen, total organic carbon, and total cell count levels in the first draw of cold water samples were similar across all buildings. However, the ranges of heavy metal concentrations in large buildings were greater than observed in small buildings. Copper (Cu), lead (Pb), and manganese (Mn) sporadically exceeded drinking water limits at cold water fixtures, with maximum concentrations of 2.7 mg Cu L-1, 45.4 μg Pb L-1, 1.9 mg Mn L-1. Flushing the plumbing for 5 min resulted in detectable residual at fixtures in three buildings, but even after 125 min of flushing in largest and oldest building, no residual chlorine was detected at the fixture closest to the building's point of entry. During the pandemic, the building owner conducted fixture flushing, where one to a few fixtures were operated per visit in buildings with hundreds of fixtures and multiple floors. However, further research is needed to understand the fundamental processes that control faucet water quality from the service line to the faucet. In the absence of this knowledge, building owners should create and use as-built drawings to develop flushing plans and conduct periodic water testing.
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Affiliation(s)
- Kyungyeon Ra
- Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN, 47907, USA
| | - Caitlin Proctor
- Agricultural and Biological Engineering, Division of Environmental and Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Christian Ley
- Division of Environmental and Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA
- Civil and Environmental Engineering, University of Colorado, 1111 Engineering Drive, Boulder, CO, 80309, USA
| | - Danielle Angert
- Division of Environmental and Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA
- Civil, Architectural and Environmental Engineering, University of Texas, 301E E Dean Keeton Street, Austin, TX, 78712, USA
| | - Yoorae Noh
- Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN, 47907, USA
| | - Tolulope Odimayomi
- Division of Environmental and Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA
- Civil and Environmental Engineering, Virginia Tech, 750 Drillfield Drive, Blacksburg, VA, 24061, USA
| | - Andrew J. Whelton
- Lyles School of Civil Engineering, Division of Environmental and Ecological Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN, 47907, USA
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8
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Wang X, Wang M, Liu X, Mao Y, Chen Y, Dai S. Surveillance-image-based outdoor air quality monitoring. Environ Sci Ecotechnol 2024; 18:100319. [PMID: 37841651 PMCID: PMC10569950 DOI: 10.1016/j.ese.2023.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 10/17/2023]
Abstract
Air pollution threatens human health, necessitating effective and convenient air quality monitoring. Recently, there has been a growing interest in using camera images for air quality estimation. However, a major challenge has been nighttime detection due to the limited visibility of nighttime images. Here we present a hybrid deep learning model, capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images. Our model, which integrates a convolutional neural network (CNN) and long short-term memory (LSTM), adeptly captures spatial-temporal image features, enabling air quality estimation at any time of day, including PM2.5 and PM10 concentrations, as well as the air quality index (AQI). Compared to independent CNN networks that solely extract spatial features, our model demonstrates superior accuracy on self-constructed datasets with R2 = 0.94 and RMSE = 5.11 μg m-3 for PM2.5, R2 = 0.92 and RMSE = 7.30 μg m-3 for PM10, and R2 = 0.94 and RMSE = 5.38 for AQI. Furthermore, our model excels in daytime air quality estimation and enhances nighttime predictions, elevating overall accuracy. Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model, reaffirming its applicability and superiority for air quality monitoring.
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Affiliation(s)
- Xiaochu Wang
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Meizhen Wang
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Xuejun Liu
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Ying Mao
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Yang Chen
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Songsong Dai
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
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9
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Yousefi S, Huang X, Poursoroush A, Majoor J, Lemij H, Vermeer K, Elze T, Wang M, Nouri-Mahdavi K, Mohammadzadeh V, Brusini P, Johnson C. An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging. Ophthalmol Sci 2024; 4:100389. [PMID: 37868793 PMCID: PMC10585627 DOI: 10.1016/j.xops.2023.100389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 08/08/2023] [Accepted: 08/18/2023] [Indexed: 10/24/2023]
Abstract
Purpose To develop an objective glaucoma damage severity classification system based on OCT-derived retinal nerve fiber layer (RNFL) thickness measurements. Design Algorithm development for RNFL damage severity classification based on multicenter OCT data. Subjects and Participants A total of 6561 circumpapillary RNFL profiles from 2269 eyes of 1171 subjects to develop models, and 2505 RNFL profiles from 1099 eyes of 900 subjects to validate models. Methods We developed an unsupervised k-means model to identify clusters of eyes with similar RNFL thickness profiles. We annotated the clusters based on their respective global RNFL thickness. We computed the optimal global RNFL thickness thresholds that discriminated different severity levels based on Bayes' minimum error principle. We validated the proposed pipeline based on an independent validation dataset with 2505 RNFL profiles from 1099 eyes of 900 subjects. Main Outcome Measures Accuracy, area under the receiver operating characteristic curve, and confusion matrix. Results The k-means clustering discovered 4 clusters with 1382, 1613, 1727, and 1839 samples with mean (standard deviation) global RNFL thickness of 58.3 (8.9) μm, 78.9 (6.7) μm, 87.7 (8.2) μm, and 101.5 (7.9) μm. The Bayes' minimum error classifier identified optimal global RNFL values of > 95 μ m , 86 to 95 μ m , 70 to 85 μ m , and < 70 μ m for discriminating normal eyes and eyes at the early, moderate, and advanced stages of RNFL thickness loss, respectively. About 4% of normal eyes and 98% of eyes with advanced RNFL loss had either global, or ≥ 1 quadrant, RNFL thickness outside of normal limits provided by the OCT instrument. Conclusions Unsupervised machine learning discovered that the optimal RNFL thresholds for separating normal eyes and eyes with early, moderate, and advanced RNFL loss were 95 μ m , 85 μm, and 70 μ m , respectively. This RNFL loss classification system is unbiased as there was no preassumption or human expert intervention in the development process. Additionally, it is objective, easy to use, and consistent, which may augment glaucoma research and day-to-day clinical practice. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Asma Poursoroush
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Julek Majoor
- Rotterdam Ophthalmic Institute, The Rotterdam Eye Hospital, Rotterdam, The Netherlands
| | - Hans Lemij
- Rotterdam Ophthalmic Institute, The Rotterdam Eye Hospital, Rotterdam, The Netherlands
| | - Koen Vermeer
- Rotterdam Ophthalmic Institute, The Rotterdam Eye Hospital, Rotterdam, The Netherlands
| | - Tobias Elze
- Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachussetts
| | - Mengyu Wang
- Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachussetts
| | - Kouros Nouri-Mahdavi
- Department of Ophthalmology, University of California Los Angeles, Los Angeles, California
| | - Vahid Mohammadzadeh
- Department of Ophthalmology, University of California Los Angeles, Los Angeles, California
| | - Paolo Brusini
- Department of Ophthalmology, “Città di Udine” Health Center, Udine, Italy
| | - Chris Johnson
- Department of Ophthalmology & Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa
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Tsuboi K, Mazloumi M, Guo Y, Wang J, Flaxel CJ, Bailey ST, Wilson DJ, Huang D, Jia Y, Hwang TS. Early Sign of Retinal Neovascularization Evolution in Diabetic Retinopathy: A Longitudinal OCT Angiography Study. Ophthalmol Sci 2024; 4:100382. [PMID: 37868804 PMCID: PMC10587637 DOI: 10.1016/j.xops.2023.100382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/12/2023] [Accepted: 08/07/2023] [Indexed: 10/24/2023]
Abstract
Purpose To assess whether the combination of en face OCT and OCT angiography (OCTA) can capture observable, but subtle, structural changes that precede clinically evident retinal neovascularization (RNV) in eyes with diabetic retinopathy (DR). Design Retrospective, longitudinal study. Participants Patients with DR that had at least 2 visits. Methods We obtained wide-field OCTA scans of 1 eye from each participant and generated en face OCT, en face OCTA, and cross-sectional OCTA. We identified eyes with RNV sprouts, defined as epiretinal hyperreflective materials on en face OCT with flow signals breaching the internal limiting membrane on the cross-sectional OCTA without recognizable RNV on en face OCTA and RNV fronds, defined as recognizable abnormal vascular structures on the en face OCTA. We examined the corresponding location from follow-up or previous visits for the presence or progression of the RNV. Main Outcome Measures The characteristics and longitudinal observation of early signs of RNV. Results From 71 eyes, we identified RNV in 20 eyes with the combination of OCT and OCTA, of which 13 (65%) were photographically graded as proliferative DR, 6 (30%) severe nonproliferative DR, and 1 (5%) moderate nonproliferative diabetic retinopathy. From these eyes, we identified 38 RNV sprouts and 26 RNV fronds at the baseline. Thirty-four RNVs (53%) originated from veins, 24 (38%) were from intraretinal microabnormalities, and 6 (9%) were from a nondilated capillary bed. At the final visit, 53 RNV sprouts and 30 RNV fronds were detected. Ten eyes (50%) showed progression, defined as having a new RNV lesion or the development of an RNV frond from an RNV sprout. Four (11%) RNV sprouts developed into RNV fronds with a mean interval of 7.0 months. Nineteen new RNV sprouts developed during the follow-up, whereas no new RNV frond was observed outside an identified RNV sprout. The eyes with progression were of younger age (P = 0.014) and tended to be treatment naive (P = 0.07) compared with eyes without progression. Conclusions Longitudinal observation demonstrated that a combination of en face OCT and cross-sectional OCTA can identify an earlier form of RNV before it can be recognized on en face OCTA. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Kotaro Tsuboi
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
- Department of Ophthalmology, Aichi Medical University, 1-1, Yazako-Karimata, Nagakute, Aichi, 480-1195, Japan
| | - Mehdi Mazloumi
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Yukun Guo
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Jie Wang
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
| | | | - Steven T. Bailey
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - David J. Wilson
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - David Huang
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Yali Jia
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
| | - Thomas S. Hwang
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
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Adamczyk P, Biczak J, Kotlarska K, Daren A, Cichocki Ł. On the specificity of figurative language comprehension impairment in schizophrenia and its relation to cognitive skills but not psychopathological symptoms - Study on metaphor, humor and irony. Schizophr Res Cogn 2024; 35:100294. [PMID: 37928747 PMCID: PMC10624582 DOI: 10.1016/j.scog.2023.100294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 11/07/2023]
Abstract
People with schizophrenia have difficulty understanding figurative expressions, such as metaphors, humor or irony. The present study investigated the specificity of figurative language impairment in schizophrenia and its relation with cognitive and psychotic symptoms. It included 54 schizophrenia and 54 age and sex-matched healthy subjects who performed a cognitive screening (ACE-III) and figurative language comprehension task consisting of 60 short stories with three types of endings: a figurative one and its literal and an absurd (meaningless) counterparts. Each figurative domain - metaphor, humor, irony - was split into two sub-domains, i.e., conventional and novel metaphors, intended-to-be-funny and social-norm-violation jokes, simple irony and critical sarcasm, respectively. The main findings are: i) in schizophrenia, figurative language deficit manifests itself in each domain; ii) the most pronounced subdomain-specific impairment has been found for novel vs conventional metaphors and irony vs sarcasm; iii) altered figurative language comprehension was related to diminished cognitive abilities but not to psychopathology symptoms (PANSS) or other clinical characteristics. This may suggest that figurative language impairment, as a specific part of communication deficit, may be regarded as an essential characteristic of schizophrenia, related to primary cognitive deficits but independent of psychopathology.
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Affiliation(s)
| | | | | | - Artur Daren
- Faculty of Psychology, Pedagogy, and Humanities, Andrzej Frycz Modrzewski Krakow University, Krakow, Poland
| | - Łukasz Cichocki
- Babinski Clinical Hospital, Krakow, Poland
- Clinic of Psychiatry, Andrzej Frycz Modrzewski Krakow University, Krakow, Poland
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12
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Bernstein IA, Koornwinder A, Hwang HH, Wang SY. Automated Recognition of Visual Acuity Measurements in Ophthalmology Clinical Notes Using Deep Learning. Ophthalmol Sci 2024; 4:100371. [PMID: 37868799 PMCID: PMC10587603 DOI: 10.1016/j.xops.2023.100371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/20/2023] [Accepted: 07/13/2023] [Indexed: 10/24/2023]
Abstract
Purpose Visual acuity (VA) is a critical component of the eye examination but is often only documented in electronic health records (EHRs) as unstructured free-text notes, making it challenging to use in research. This study aimed to improve on existing rule-based algorithms by developing and evaluating deep learning models to perform named entity recognition of different types of VA measurements and their lateralities from free-text ophthalmology notes: VA for each of the right and left eyes, with and without glasses correction, and with and without pinhole. Design Cross-sectional study. Subjects A total of 319 756 clinical notes with documented VA measurements from approximately 90 000 patients were included. Methods The notes were split into train, validation, and test sets. Bidirectional Encoder Representations from Transformers (BERT) models were fine-tuned to identify VA measurements from the progress notes and included BERT models pretrained on biomedical literature (BioBERT), critical care EHR notes (ClinicalBERT), both (BlueBERT), and a lighter version of BERT with 40% fewer parameters (DistilBERT). A baseline rule-based algorithm was created to recognize the same VA entities to compare against BERT models. Main Outcome Measures Model performance was evaluated on a held-out test set using microaveraged precision, recall, and F1 score for all entities. Results On the human-annotated subset, BlueBERT achieved the best microaveraged F1 score (F1 = 0.92), followed by ClinicalBERT (F1 = 0.91), DistilBERT (F1 = 0.90), BioBERT (F1 = 0.84), and the baseline model (F1 = 0.83). Common errors included labeling VA in sections outside of the examination portion of the note, difficulties labeling current VA alongside a series of past VAs, and missing nonnumeric VAs. Conclusions This study demonstrates that deep learning models are capable of identifying VA measurements from free-text ophthalmology notes with high precision and recall, achieving significant performance improvements over a rule-based algorithm. The ability to recognize VA from free-text notes would enable a more detailed characterization of ophthalmology patient cohorts and enhance the development of models to predict ophthalmology outcomes. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Isaac A. Bernstein
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California
| | - Abigail Koornwinder
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California
| | - Hannah H. Hwang
- Department of Ophthalmology, Weill Cornell Medicine, New York, New York
| | - Sophia Y. Wang
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California
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13
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Liu K, Li Q, Andrady AL, Wang X, He Y, Li D. Underestimated activity-based microplastic intake under scenario-specific exposures. Environ Sci Ecotechnol 2024; 18:100316. [PMID: 37860830 PMCID: PMC10583090 DOI: 10.1016/j.ese.2023.100316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 10/21/2023]
Abstract
Despite increasing alarms over the health impacts of microplastics (MPs) due to their detection in human organs and feces, precise exposure evaluations remain scarce. To comprehend their risks, there is a distinct need to prioritize quantitive estimates in MP exposome, particularly at the environmentally-realistic level. Here we used a method rooted in real-world MP measurements and activity patterns to determine the daily intake of MPs through inhalation and from ground dust/soil ingestion. We found that nearly 80% of this intake comes from residential sectors, with activity intensity and behavioral types significantly affecting the human MP burden. The data showed a peak in MP exposure for those aged 18-64. When compared to dietary MP intake sources like seafood, salt, and water, we identified a previously underestimated exposure from inhalation and dust/soil ingestion, emphasizing the need for more realistic evaluations that incorporate activity factors. This discovery raises questions about the accuracy of past studies and underscores MP's potential health risks. Moreover, our time-based simulations revealed increased MP intake during the COVID-19 lockdown due to more surface dust ingestion, shedding light on how global health crises may inadvertently elevate MP exposure risks.
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Affiliation(s)
- Kai Liu
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai, 200062, China
- Plastic Marine Debris Research Center, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
- Regional Training and Research Center on Plastic Marine Debris and Microplastics, IOC-UNESCO, 500 Dongchuan Road, Shanghai, 200241, China
| | - Qingqing Li
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai, 200062, China
- Regional Training and Research Center on Plastic Marine Debris and Microplastics, IOC-UNESCO, 500 Dongchuan Road, Shanghai, 200241, China
| | - Anthony L. Andrady
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27695, USA
| | - Xiaohui Wang
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai, 200062, China
- Plastic Marine Debris Research Center, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
- Regional Training and Research Center on Plastic Marine Debris and Microplastics, IOC-UNESCO, 500 Dongchuan Road, Shanghai, 200241, China
| | - Yinan He
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai, 200062, China
- Plastic Marine Debris Research Center, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
- Regional Training and Research Center on Plastic Marine Debris and Microplastics, IOC-UNESCO, 500 Dongchuan Road, Shanghai, 200241, China
| | - Daoji Li
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai, 200062, China
- Plastic Marine Debris Research Center, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
- Regional Training and Research Center on Plastic Marine Debris and Microplastics, IOC-UNESCO, 500 Dongchuan Road, Shanghai, 200241, China
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14
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Li AS, Myers J, Stinnett SS, Grewal DS, Jaffe GJ. Gradeability and Reproducibility of Geographic Atrophy Measurement in GATHER-1, a Phase II/III Randomized Interventional Trial. Ophthalmol Sci 2024; 4:100383. [PMID: 37868797 PMCID: PMC10587635 DOI: 10.1016/j.xops.2023.100383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/24/2023] [Accepted: 08/07/2023] [Indexed: 10/24/2023]
Abstract
Objective To validate GATHER-1 inclusion criteria and the study's primary anatomic end point by assessing the reproducibility of geographic atrophy (GA) measurements and factors that affect reproducibility. Design Post hoc analysis of phase II/III clinical trial. Subjects All 286 participants included in the GATHER-1 study. Methods For each subject, blue-light fundus autofluorescence (FAF), color fundus photographs, fluorescein angiograms, and OCT scans were obtained on the study eye and fellow eye. Geographic atrophy area and other lesion characteristics were independently graded by 2 experienced primary readers. If the 2 readers differed on gradeability, GA area (> 10%) or other lesion characteristics, the image was graded by an arbitrator whose measurement or characterization was the final grade. Main Outcome Measures The main outcome measures were gradeability and reproducibility of FAF imaging data. Imaging data included lesion area, confluence of GA with peripapillary atrophy (PPA), whether GA involved the foveal centerpoint, and type of hyperautofluorescence pattern. Results A total of 2004 images (1002 visits, 286 participants) were analyzed. Gradeability (90.5%) and interreader gradeability concordance (90.2%) were high across all visits. Study eye images were more gradable compared with fellow-eye images. A greater proportion of smaller lesions required arbitration, but interreader reproducibility was consistently high for all images. There was no difference in gradeability, gradeability concordance, or lesion-area concordance for images with PPA-confluent GA compared with those with nonconfluent PPA. Foveal centerpoint-involving lesions had lower gradeability and lesion-area concordance. Images with diffuse patterns of hyperautofluorescence had better gradeability and gradeability concordance than those with nondiffuse patterns but had no difference in lesion-area or lesion-area concordance. Conclusions There is high gradeability and excellent reproducibility measures across all images. These data support the validity of conclusions from GATHER-1 and the chosen inclusion criteria and end point. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Angela S. Li
- Department of Ophthalmology, Duke University, Durham, North Carolina
| | - Justin Myers
- Department of Ophthalmology, Duke University, Durham, North Carolina
| | | | - Dilraj S. Grewal
- Department of Ophthalmology, Duke University, Durham, North Carolina
| | - Glenn J. Jaffe
- Department of Ophthalmology, Duke University, Durham, North Carolina
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15
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Cai QL, Huang CY, Tong L, Zhong N, Dai XR, Li JR, Zheng J, He MM, Xiao H. Sampling efficiency of a polyurethane foam air sampler: Effect of temperature. Environ Sci Ecotechnol 2024; 18:100327. [PMID: 37908224 PMCID: PMC10613919 DOI: 10.1016/j.ese.2023.100327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 11/02/2023]
Abstract
Effective monitoring of atmospheric concentrations is vital for assessing the Stockholm Convention's effectiveness on persistent organic pollutants (POPs). This task, particularly challenging in polar regions due to low air concentrations and temperature fluctuations, requires robust sampling techniques. Furthermore, the influence of temperature on the sampling efficiency of polyurethane foam discs remains unclear. Here we employ a flow-through sampling (FTS) column coupled with an active pump to collect air samples at varying temperatures. We delved into breakthrough profiles of key pollutants, such as polycyclic aromatic hydrocarbons (PAHs), polychlorobiphenyls (PCBs), and organochlorine pesticides (OCPs), and examined the temperature-dependent behaviors of the theoretical plate number (N) and breakthrough volume (VB) using frontal chromatography theory. Our findings reveal a significant relationship between temperature dependence coefficients (KTN, KTV) and compound volatility, with decreasing values as volatility increases. While distinct trends are noted for PAHs, PCBs, and OCPs in KTN, KTV values exhibit similar patterns across all chemicals. Moreover, we establish a binary linear correlation between log (VB/m3), 1/(T/K), and N, simplifying breakthrough level estimation by enabling easy conversion between N and VB. Finally, an empirical linear solvation energy relationship incorporating a temperature term is developed, yielding satisfactory results for N at various temperatures. This approach holds the potential to rectify temperature-related effects and loss rates in historical data from long-term monitoring networks, benefiting polar and remote regions.
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Affiliation(s)
- Qiu-Liang Cai
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
- Key Laboratory of Ecological Environment Analysis and Pollution Control in Western Guangxi Region, College of Agriculture and Food Engineering, Baise University, Baise, 533000, China
| | - Cen-Yan Huang
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo, 315100, China
| | - Lei Tong
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315830, China
| | - Ning Zhong
- Minnan Normal University, Zhangzhou, 363000, China
| | - Xiao-Rong Dai
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo, 315100, China
| | - Jian-Rong Li
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315830, China
| | - Jie Zheng
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315830, China
| | - Meng-Meng He
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
- Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315830, China
| | - Hang Xiao
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
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16
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Lee AM, Xu TT, Starr MR. Trends in Research Payments for Diabetic Macular Edema from 2015 to 2021. Ophthalmol Sci 2024; 4:100379. [PMID: 37868798 PMCID: PMC10587623 DOI: 10.1016/j.xops.2023.100379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/12/2023] [Accepted: 08/02/2023] [Indexed: 10/24/2023]
Abstract
Purpose To evaluate characteristics of research payments for diabetic macular edema (DME) studies and correlations to current management trends. Design Retrospective cross-sectional study. Subjects Research payments for DME. Methods Studies with keywords of "diabetic macular edema" or "DME" in the title were extracted from the Centers of Medicare & Medicaid Services Open Payments database from 2015 to 2021. Recipients, payors, and payment amounts were identified. Industry funding was compared with public research funding by the National Eye Institute (NEI). Main Outcome Measures Trends and total value of industry and public fundings for DME from 2015 to 2021. Results From 2015 to 2021, 451 beneficiaries received 6062 industry payments for a total of $120 148 997.41 for DME-related research. The total value of industry funding increased from $8 225 859.08 in 2015 to $50 092 778.45 in 2021. Of the 6062 industry payments, 5367 (88.5%) were reported by male recipients compared with 695 (11.5%) female beneficiaries. Payments to female recipients increased from 60 (7.1%) in 2015 to 335 (13.7%) in 2021. In comparison, public funding for DME-related research from the NEI was comprised of $18 863 266.00 to 17 principal investigators from 2015 to 2021. The total value of public funding increased from $973 590.00 in 2015 to $3 354 376.00 in 2021. Of 59 public research payments, 46 (78.0%) were reported by male recipients and 13 (22.0%) by female recipients. Payments to female recipients increased from 1 (25.0%) in 2015 to 3 (30.0%) in 2021. The most highly invested product by industry were anti-VEGF agents, accounting for $89 955 595.20 (74.9%) of total payment value. Conclusions There was an increase in both industry and public-sponsored funding for DME-related research from 2015 to 2021. There seemed to be a possible discrepancy in both industry and public funding based on sex for DME studies during the study period. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- April M. Lee
- SUNY Downstate College of Medicine, Brooklyn, New York
| | - Timothy T. Xu
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota
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Abdelmotaal H, Hazarbassanov RM, Salouti R, Nowroozzadeh MH, Taneri S, Al-Timemy AH, Lavric A, Yousefi S. Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning. Ophthalmol Sci 2024; 4:100380. [PMID: 37868800 PMCID: PMC10587634 DOI: 10.1016/j.xops.2023.100380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 07/21/2023] [Accepted: 08/04/2023] [Indexed: 10/24/2023]
Abstract
Objective To assess the performance of convolutional neural networks (CNNs) for automated detection of keratoconus (KC) in standalone Scheimpflug-based dynamic corneal deformation videos. Design Retrospective cohort study. Participants We retrospectively analyzed datasets with records of 734 nonconsecutive, refractive surgery candidates, and patients with unilateral or bilateral KC. Methods We first developed a video preprocessing pipeline to translate dynamic corneal deformation videos into 3-dimensional pseudoimage representations and then trained a CNN to directly identify KC from pseudoimages. We calculated the model's KC probability score cut-off and evaluated the performance by subjective and objective accuracy metrics using 2 independent datasets. Main Outcome Measures Area under the receiver operating characteristics curve (AUC), accuracy, specificity, sensitivity, and KC probability score. Results The model accuracy on the test subset was 0.89 with AUC of 0.94. Based on the external validation dataset, the AUC and accuracy of the CNN model for detecting KC were 0.93 and 0.88, respectively. Conclusions Our deep learning-based approach was highly sensitive and specific in separating normal from keratoconic eyes using dynamic corneal deformation videos at levels that may prove useful in clinical practice. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
| | - Rossen Mihaylov Hazarbassanov
- Hospital de Olhos-CRO, Guarulhos, São Paulo, Brazil
- Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of São Paulo, São Paulo, Brazil
| | - Ramin Salouti
- Poostchi Ophthalmology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Suphi Taneri
- Ruhr University, Bochum, Germany
- Zentrum für Refraktive Chirurgie, Muenster, Germany
| | - Ali H. Al-Timemy
- Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
| | - Alexandru Lavric
- Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, Suceava, Romania
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee
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Roberts CJ, Knoll KM, Mahmoud AM, Hendershot AJ, Yuhas PT. Corneal Stress Distribution Evolves from Thickness-Driven in Normal Corneas to Curvature-Driven with Progression in Keratoconus. Ophthalmol Sci 2024; 4:100373. [PMID: 37868791 PMCID: PMC10587627 DOI: 10.1016/j.xops.2023.100373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 10/24/2023]
Abstract
Purpose To introduce the novel parameter of Corneal Contribution to Stress (CCS) and compare stress distribution patterns between keratoconus (KCN) and normal corneas. Design Prospective, observational, cross-sectional study. Participants The study included 66 eyes of 40 subjects diagnosed with KCN and 155 left eyes from 155 normal control (NRL) subjects. Methods Tomography was obtained to calculate the newly proposed CCS, defined according to the hoop stress formula without intraocular pressure, R/2t, where R is the radius of curvature and t is the thickness. CCS maps were calculated from pachymetry and tangential curvature maps. Custom software identified the 2-mm-diameter zones of greatest curvature (Cspot-max), thinnest pachymetry (Pach-min), greatest stress (CCSmax), and lowest stress (CCSmin). Stress difference (CCSdiff) was calculated as CCSmax - CCSmin. Distances between Cspot-max vs. Pach-min, vs. CCSmax, and vs. CCSmin, as well as between Pach-min vs. CCSmax and vs. CCSmin, were calculated. t tests were performed between cohorts, and paired t tests were performed within cohorts. Univariate linear regression analyses were performed between parameters and distances. The significance threshold was P < 0.05. Main Outcome Measures Corneal stress parameters, corneal features of maximum curvature, minimum thickness, and distances between corneal stress parameters and corneal features. Results CCSmax was significantly closer to Pach-min (0.79 ± 0.92) and Cspot-max (2.04 ± 0.85) than CCSmin (3.17 ± 0.38, 2.73 ± 1.53, respectively) in NRL, P < 0.0001, whereas CCSmin was significantly closer to Cspot-max (1.35 ± 1.43) than CCSmax (2.52 ± 0.72) in KCN, P < 0.0001. Cspot-max (severity) was significantly related to CCSdiff in KCN (P < 0.0001; R2 = 0.5882) with a weak relationship in NRL (P < 0.0080, R2 = 0.0451). Cspot-max was significantly related to the distance from Pach-min to CCSmax (P < 0.0001; R2 = 0.3737) without significance in NRL (P = 0.8011). Conclusions Corneal stress is driven by thickness in NRL, with greatest stress at thinnest pachymetry and greatest curvature. However, maximum stress moves away from thinnest pachymetry with progression in KCN, and minimum stress is associated with maximum curvature. Severity in KCN is significantly related to greater difference between maximum and minimum stress, consistent with the biomechanical cycle of decompensation. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Cynthia J. Roberts
- Department of Ophthalmology & Visual Sciences, College of Medicine, The Ohio State University, Columbus, Ohio
- Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, Ohio
| | - Kayla M. Knoll
- Department of Ophthalmology & Visual Sciences, College of Medicine, The Ohio State University, Columbus, Ohio
- College of Optometry, The Ohio State University, Columbus, Ohio
| | - Ashraf M. Mahmoud
- Department of Ophthalmology & Visual Sciences, College of Medicine, The Ohio State University, Columbus, Ohio
- Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, Ohio
| | - Andrew J. Hendershot
- Department of Ophthalmology & Visual Sciences, College of Medicine, The Ohio State University, Columbus, Ohio
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Zhao C, Liu X, Tan H, Yin S, Su L, Du B, Khalid M, Sinkkonen A, Hui N. Neighborhood garden's age shapes phyllosphere microbiota associated with respiratory diseases in cold seasons. Environ Sci Ecotechnol 2024; 18:100315. [PMID: 37886031 PMCID: PMC10598728 DOI: 10.1016/j.ese.2023.100315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 10/28/2023]
Abstract
Neighborhood gardens serve as sensitive sites for human microbial encounters, with phyllosphere microbes directly impacting our respiratory health. Yet, our understanding remains limited on how factors like season, garden age, and land use shape the risk of respiratory diseases (RDs) tied to these garden microbes. Here we examined the microbial communities within the phyllosphere of 72 neighborhood gardens across Shanghai, spanning different seasons (warm and cold), garden ages (old and young), and locales (urban and rural). We found a reduced microbial diversity during the cold season, except for Gammaproteobacteria which exhibited an inverse trend. While land use influenced the microbial composition, urban and rural gardens had strikingly similar microbial profiles. Alarmingly, young gardens in the cold season hosted a substantial proportion of RDs-associated species, pointing towards increased respiratory inflammation risks. In essence, while newer gardens during colder periods show a decline in microbial diversity, they have an increased presence of RDs-associated microbes, potentially escalating respiratory disease prevalence. This underscores the pivotal role the garden age plays in enhancing both urban microbial diversity and respiratory health.
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Affiliation(s)
- Chang Zhao
- School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Rd., 200240, Shanghai, China
- Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station, Ministry of Science and Technology, Ministry of Education, 800 Dongchuan Rd., 200240, Shanghai, China
- Shanghai Urban Forest Ecosystem Research Station, National Forestry and Grassland Administration, 800 Dongchuan Rd., 200240, Shanghai, China
| | - Xinxin Liu
- School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Rd., 200240, Shanghai, China
- Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station, Ministry of Science and Technology, Ministry of Education, 800 Dongchuan Rd., 200240, Shanghai, China
- Shanghai Urban Forest Ecosystem Research Station, National Forestry and Grassland Administration, 800 Dongchuan Rd., 200240, Shanghai, China
| | - Haoxin Tan
- School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Rd., 200240, Shanghai, China
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Lahti, Finland
| | - Shan Yin
- School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Rd., 200240, Shanghai, China
| | - Lantian Su
- School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Rd., 200240, Shanghai, China
| | - Baoming Du
- School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Rd., 200240, Shanghai, China
| | - Muhammad Khalid
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, Wenzhou, China
| | - Aki Sinkkonen
- Department of Garden Technologies, Horticulture Technologies, Natural Resources Institute Finland, Helsinki, Finland
| | - Nan Hui
- School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Rd., 200240, Shanghai, China
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Lahti, Finland
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Hammer M, Heggemann Y, Auffarth GU. Dynamic Stimulation Aberrometry: Objectively Measured Accommodation and Pupil Dynamics after Phakic Iris-Fixated Intraocular Lens Implantation. Ophthalmol Sci 2024; 4:100374. [PMID: 37868795 PMCID: PMC10587632 DOI: 10.1016/j.xops.2023.100374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 10/24/2023]
Abstract
Purpose Anterior iris-claw phakic intraocular lens (pIOL) implantation is a treatment option for refractive, ametropic patients. However, the postoperative accommodative ability has not been systematically researched. Dynamic stimulation aberrometry allows the objective and dynamical measurement of accommodation by observing ocular aberrations during the accommodation process. We investigated the dynamic accommodative ability after pIOL implantation compared with a healthy age- and gender-matched control group. Design Clinical, comparative case-control study. Subjects We included patients aged 18-50 years that either underwent pIOL implantation > 1 month ago or served as a healthy, phakic control group. Methods The accommodative ability and pupil dynamics of both groups were investigated using dynamic stimulation aberrometry. The method allows the analysis of dynamic parameters during accommodation, such as the accommodation speed. A 1:1 propensity score matching was conducted based on the patients' age and gender. Main Outcome Measures Parameters of objective accommodation, such as accommodative amplitude and pupil dynamic during accommodation. Results Fifty-eight healthy, phakic eyes < 50 years of age and 21 eyes after pIOL implantation to correct myopia (pIOL, Verisyse, AMO, Inc) were enrolled. Patients that underwent anterior pIOL implantation were examined on average 24 ± 18 months after surgery. After matching, the mean age of both groups was not significantly different (35 ± 8 vs. 34 ± 8 years). No significant difference in dynamic parameters of accommodation or the accommodative amplitude (2.8 ± 1.4 and 2.9 ± 1.4 diopters [D] for pIOL and control group, P = 0.82) were seen. Maximum and minimum pupil sizes were not significantly different. The change in pupil size during deaccommodation was significantly faster in patients after pIOL implantation (P < 0.001). Conclusions Dynamic stimulation aberrometry allowed the objective, dynamic, measurement of wavefronts in subjects with accommodative amplitudes up to 7 D. Phakic intraocular lens implantation does not impair the accommodative ability. It alters pupil dynamics during deaccommodation. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Maximilian Hammer
- David J. Apple Laboratory for Vision Research, Heidelberg, Germany
- Department of Ophthalmology, University of Heidelberg, International Vision Correction Research Centre, Heidelberg, Germany
| | - Yvonne Heggemann
- David J. Apple Laboratory for Vision Research, Heidelberg, Germany
- Department of Ophthalmology, University of Heidelberg, International Vision Correction Research Centre, Heidelberg, Germany
| | - Gerd U. Auffarth
- David J. Apple Laboratory for Vision Research, Heidelberg, Germany
- Department of Ophthalmology, University of Heidelberg, International Vision Correction Research Centre, Heidelberg, Germany
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Bartley MC, Tremblay T, De Silva AO, Michelle Kamula C, Ciastek S, Kuzyk ZZA. Sedimentary records of contaminant inputs in Frobisher Bay, Nunavut. Environ Sci Ecotechnol 2024; 18:100313. [PMID: 37860827 PMCID: PMC10582354 DOI: 10.1016/j.ese.2023.100313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 09/05/2023] [Accepted: 09/11/2023] [Indexed: 10/21/2023]
Abstract
Contaminants, such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), heavy metals, and per and polyfluoroalkyl substances (PFASs), primarily reach the Arctic through long-range atmospheric and oceanic transport. However, local sources within the Arctic also contribute to the levels observed in the environment, including legacy sources and new sources that arise from activities associated with increasing commercial and industrial development. The City of Iqaluit in Frobisher Bay, Nunavut (Canada), has seen rapid population growth and associated development during recent decades yet remains a site of interest for ocean protection, where Inuit continue to harvest country food. In the present study, seven dated marine sediment cores collected in Koojesse Inlet near Iqaluit, and from sites in inner and outer Frobisher Bay, respectively, were analyzed for total mercury (THg), major and trace elements, PAHs, PCBs, and PFASs. The sedimentary record in Koojesse Inlet shows a period of Aroclor 1260-like PCB input concurrent with military site presence in the 1950-60s, followed by decades of input of pyrogenic PAHs, averaging about ten times background levels. Near-surface sediments in Koojesse Inlet also show evidence of transient local-source inputs of THg and PFASs, and recycling or continued slow release of PCBs from legacy land-based sources. Differences in PFAS congener composition clearly distinguish the local sources from long-range transport. Outside Koojesse Inlet but still in inner Frobisher Bay, 9.2 km from Iqaluit, sediments showed evidence of both local source (PCB) and long-range transport. In outer Frobisher Bay, an up-core increase in THg and PFASs in sediments may be explained by ongoing inputs of these contaminants from long-range transport. The context for ocean protection and country food harvesting in this region of the Arctic clearly involves both local sources and long-range transport, with past human activities leaving a long legacy insofar as levels of persistent organic pollutants are concerned.
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Affiliation(s)
- Meaghan C. Bartley
- Centre for Earth Observation Science, Clayton H. Riddell Faculty of Environment, Earth and Resources, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
- Department of Environment and Geography, Clayton H. Riddell Faculty of Environment, Earth and Resources, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
| | - Tommy Tremblay
- Canada-Nunavut Geoscience Office, Iqaluit, Nunavut, X0A 0H0, Canada
| | - Amila O. De Silva
- Environment and Climate Change Canada, Burlington, Ontario, L7S 1A1, Canada
| | - C. Michelle Kamula
- Centre for Earth Observation Science, Clayton H. Riddell Faculty of Environment, Earth and Resources, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
| | - Stephen Ciastek
- Centre for Earth Observation Science, Clayton H. Riddell Faculty of Environment, Earth and Resources, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
| | - Zou Zou A. Kuzyk
- Centre for Earth Observation Science, Clayton H. Riddell Faculty of Environment, Earth and Resources, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
- Department of Environment and Geography, Clayton H. Riddell Faculty of Environment, Earth and Resources, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
- Department of Earth Sciences, Clayton H. Riddell Faculty of Environment, Earth and Resources, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
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Li YF, Hao S, Ma WL, Yang PF, Li WL, Zhang ZF, Liu LY, Macdonald RW. Persistent organic pollutants in global surface soils: Distributions and fractionations. Environ Sci Ecotechnol 2024; 18:100311. [PMID: 37712051 PMCID: PMC10498191 DOI: 10.1016/j.ese.2023.100311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 07/30/2023] [Accepted: 08/17/2023] [Indexed: 09/16/2023]
Abstract
The distribution and fractionation of persistent organic pollutants (POPs) in different matrices refer to how these pollutants are dispersed and separated within various environmental compartments. This is a significant study area as it helps us understand the transport efficiencies and long-range transport potentials of POPs to enter remote areas, particularly polar regions. This study provides a comprehensive review of the progress in understanding the distribution and fractionation of POPs. We focus on the contributions of four intermedia processes (dry and wet depositions for gaseous and particulate POPs) and determine their transfer between air and soil. These processes are controlled by their partitioning between gaseous and particulate phases in the atmosphere. The distribution patterns and fractionations can be categorized into primary and secondary types. Equations are developed to quantificationally study the primary and secondary distributions and fractionations of POPs. The analysis results suggest that the transfer of low molecular weight (LMW) POPs from air to soil is mainly through gas diffusion and particle deposition, whereas high molecular weight (HMW) POPs are mainly via particle deposition. HMW-POPs tend to be trapped near the source, whereas LMW-POPs are more prone to undergo long-range atmospheric transport. This crucial distinction elucidates the primary reason behind their temperature-independent primary fractionation. However, the secondary distribution and fractionation can only be observed along a temperature gradient, such as latitudinal or altitudinal transects. An animation is produced by a one-dimensional transport model to simulate conceptively the transport of CB-28 and CB-180, revealing the similarities and differences between the primary and secondary distributions and fractionations. We suggest that the decreasing temperature trend along latitudes is not the major reason for POPs to be fractionated into the polar ecosystems, but drives the longer-term accumulation of POPs in cold climates or polar cold trapping.
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Affiliation(s)
- Yi-Fan Li
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, China
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, Harbin Institute of Technology (PA-HIT), Harbin, 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin, 150090, China
- IJRC-PTS-NA, Toronto, ON, M2J 3N8, Canada
| | - Shuai Hao
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, China
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, Harbin Institute of Technology (PA-HIT), Harbin, 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin, 150090, China
| | - Wan-Li Ma
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, China
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, Harbin Institute of Technology (PA-HIT), Harbin, 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin, 150090, China
| | - Pu-Fei Yang
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, China
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, Harbin Institute of Technology (PA-HIT), Harbin, 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin, 150090, China
| | - Wen-Long Li
- College of the Environment and Ecology, Xiamen University, Xiamen, China
| | - Zi-Feng Zhang
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, China
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, Harbin Institute of Technology (PA-HIT), Harbin, 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin, 150090, China
| | - Li-Yan Liu
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, China
- International Joint Research Center for Arctic Environment and Ecosystem (IJRC-AEE), Polar Academy, Harbin Institute of Technology (PA-HIT), Harbin, 150090, China
- Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem (HPKL-PEE), Harbin, 150090, China
| | - Robie W. Macdonald
- Institute of Ocean Sciences, Department of Fisheries and Oceans, P.O. Box 6000, Sidney, BC, V8L 4B2, Canada
- Centre for Earth Observation Science, University of Manitoba, Winnipeg, R3T 2N2, Canada
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Li Z, Lan S, Zhu M. Piezoelectricity activates persulfate for water treatment: A perspective. Environ Sci Ecotechnol 2024; 18:100329. [PMID: 37886032 PMCID: PMC10598685 DOI: 10.1016/j.ese.2023.100329] [Citation(s) in RCA: 0] [Impact Index P |