151
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Wang S, Tong Y, Fan Y, Liu H, Wu J, Wang Z, Fang C. Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility. Sci Rep 2021; 11:14691. [PMID: 34282180 PMCID: PMC8289815 DOI: 10.1038/s41598-021-94060-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023] Open
Abstract
Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google's mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.
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Affiliation(s)
- Shaobin Wang
- grid.9227.e0000000119573309Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Yun Tong
- grid.428986.90000 0001 0373 6302School of Tourism, Hainan University, Haikou, China
| | - Yupeng Fan
- grid.9227.e0000000119573309Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Haimeng Liu
- grid.9227.e0000000119573309Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Jun Wu
- grid.266093.80000 0001 0668 7243Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, USA
| | - Zheye Wang
- grid.21940.3e0000 0004 1936 8278Kinder Institute for Urban Research, Rice University, Houston, USA
| | - Chuanglin Fang
- grid.9227.e0000000119573309Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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152
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Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge. J Comput Aided Mol Des 2021; 35:901-909. [PMID: 34273053 PMCID: PMC8367913 DOI: 10.1007/s10822-021-00405-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/22/2021] [Indexed: 12/22/2022]
Abstract
Accurate prediction of lipophilicity—logP—based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps that were taken to construct a novel machine learning model that can predict and generalize well. This model is based on the recently described Directed-Message Passing Neural Networks (D-MPNNs). Further enhancements included: both the inclusion of additional datasets from ChEMBL (RMSE improvement of 0.03), and the addition of helper tasks (RMSE improvement of 0.04). To the best of our knowledge, the concept of adding predictions from other models (Simulations Plus logP and logD@pH7.4, respectively) as helper tasks is novel and could be applied in a broader context. The final model that we constructed and used to participate in the challenge ranked 2/17 ranked submissions with an RMSE of 0.66, and an MAE of 0.48 (submission: Chemprop). On other datasets the model also works well, especially retrospectively applied to the SAMPL6 challenge where it would have ranked number one out of all submissions (RMSE of 0.35). Despite the fact that our model works well, we conclude with suggestions that are expected to improve the model even further.
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153
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Ter-Mikaelian MT, Gonsamo A, Chen JM, Mo G, Chen J. Historical and future carbon stocks in forests of northern Ontario, Canada. CARBON BALANCE AND MANAGEMENT 2021; 16:21. [PMID: 34264423 PMCID: PMC8281711 DOI: 10.1186/s13021-021-00184-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Forests in the Far North of Ontario (FNO), Canada, are likely the least studied in North America, and quantifying their current and future carbon (C) stocks is the first step in assessing their potential role in climate change mitigation. Although the FNO forests are unmanaged, the latter task is made more important by growing interest in developing the region's natural resources, primarily for timber harvesting. In this study, we used a combination of field and remotely sensed observations with a land surface model to estimate forest C stocks in the FNO forests and to project their future dynamics. The specific objective was to simulate historical C stocks for 1901-2014 and future C stocks for 2015-2100 for five shared socioeconomic pathway (SSP) scenarios selected as high priority scenarios for the 6th Assessment Report on Climate Change. RESULTS Carbon stocks in live vegetation in the FNO forests remained relatively stable between 1901 and 2014 while soil organic carbon (SOC) stocks steadily declined, losing about 16% of their initial value. At the end of the historical simulation (in 2014), the stocks were estimated at 19.8, 46.4, and 66.2 tCha-1 in live vegetation, SOC, and total ecosystem pools, respectively. Projections for 2015-2100 indicated effectively no substantial change in SOC stocks, while live vegetation C stocks increased, accelerating their growth in the second half of the twenty-first century. These results were consistent among all simulated SSP scenarios. Consequently, increase in total forest ecosystem C stocks by 2100 ranged from 16.7 to 20.7% of their value in 2015. Simulations with and without wildfires showed the strong effect of fire on forest C stock dynamics during 2015-2100: inclusion of wildfires reduced the live vegetation increase by half while increasing the SOC pool due to higher turnover of vegetation C to SOC. CONCLUSIONS Forest ecosystem C stock estimates at the end of historical simulation period were at the lower end but within the range of values reported in the literature for northern boreal forests. These estimates may be treated as conservatively low since the area included in the estimates is poorly studied and some of the forests may be on peat deposits rather than mineral soils. Future C stocks were projected to increase in all simulated SSP scenarios, especially in the second half of the twenty-first century. Thus, during the projected period forest ecosystems of the FNO are likely to act as a C sink. In light of growing interest in developing natural resources in the FNO, collecting more data on the status and dynamics of its forests is needed to verify the above-presented estimates and design management activities that would maintain their projected C sink status.
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Affiliation(s)
- Michael T Ter-Mikaelian
- Ontario Forest Research Institute, Ontario Ministry of Natural Resources and Forestry, 1235 Queen Street E., Sault Ste. Marie, ON, P6A 2E5, Canada.
| | - Alemu Gonsamo
- School of Earth, Environment & Society, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4S4, Canada
| | - Jing M Chen
- Department of Geography and Planning, University of Toronto, 100 St. George St, Toronto, ON, M5S 3G3, Canada
| | - Gang Mo
- Department of Geography and Planning, University of Toronto, 100 St. George St, Toronto, ON, M5S 3G3, Canada
| | - Jiaxin Chen
- Ontario Forest Research Institute, Ontario Ministry of Natural Resources and Forestry, 1235 Queen Street E., Sault Ste. Marie, ON, P6A 2E5, Canada
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154
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Berry I, O'Neill M, Sturrock SL, Wright JE, Acharya K, Brankston G, Harish V, Kornas K, Maani N, Naganathan T, Obress L, Rossi T, Simmons AE, Van Camp M, Xie X, Tuite AR, Greer AL, Fisman DN, Soucy JPR. A sub-national real-time epidemiological and vaccination database for the COVID-19 pandemic in Canada. Sci Data 2021; 8:173. [PMID: 34267221 PMCID: PMC8282612 DOI: 10.1038/s41597-021-00955-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 05/20/2021] [Indexed: 12/03/2022] Open
Abstract
The COVID-19 pandemic has demonstrated the need for real-time, open-access epidemiological information to inform public health decision-making and outbreak control efforts. In Canada, authority for healthcare delivery primarily lies at the provincial and territorial level; however, at the outset of the pandemic no definitive pan-Canadian COVID-19 datasets were available. The COVID-19 Canada Open Data Working Group was created to fill this crucial data gap. As a team of volunteer contributors, we collect daily COVID-19 data from a variety of governmental and non-governmental sources and curate a line-list of cases and mortality for all provinces and territories of Canada, including information on location, age, sex, travel history, and exposure, where available. We also curate time series of COVID-19 recoveries, testing, and vaccine doses administered and distributed. Data are recorded systematically at a fine sub-national scale, which can be used to support robust understanding of COVID-19 hotspots. We continue to maintain this dataset, and an accompanying online dashboard, to provide a reliable pan-Canadian COVID-19 resource to researchers, journalists, and the general public.
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Affiliation(s)
- Isha Berry
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
| | - Meghan O'Neill
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Shelby L Sturrock
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - James E Wright
- Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Kamal Acharya
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Gabrielle Brankston
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Vinyas Harish
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Kathy Kornas
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Nika Maani
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Thivya Naganathan
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Lindsay Obress
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Tanya Rossi
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Alison E Simmons
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Matthew Van Camp
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Xiao Xie
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Ashleigh R Tuite
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Amy L Greer
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - David N Fisman
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Jean-Paul R Soucy
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
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155
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Tutuko B, Nurmaini S, Tondas AE, Rachmatullah MN, Darmawahyuni A, Esafri R, Firdaus F, Sapitri AI. AFibNet: an implementation of atrial fibrillation detection with convolutional neural network. BMC Med Inform Decis Mak 2021; 21:216. [PMID: 34261486 PMCID: PMC8281594 DOI: 10.1186/s12911-021-01571-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/29/2021] [Indexed: 11/27/2022] Open
Abstract
Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R–R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. Result Meanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity. Conclusion These findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment
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Affiliation(s)
- Bambang Tutuko
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia.
| | - Alexander Edo Tondas
- Department of Cardiology and Vascular Medicine, Dr. Mohammad Hoesin Hospital, Palembang, Indonesia
| | - Muhammad Naufal Rachmatullah
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Annisa Darmawahyuni
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Ria Esafri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Firdaus Firdaus
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Ade Iriani Sapitri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
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156
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Kiyasseh D, Zhu T, Clifton D. A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions. Nat Commun 2021; 12:4221. [PMID: 34244504 PMCID: PMC8270996 DOI: 10.1038/s41467-021-24483-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 06/11/2021] [Indexed: 11/28/2022] Open
Abstract
Deep learning algorithms trained on instances that violate the assumption of being independent and identically distributed (i.i.d.) are known to experience destructive interference, a phenomenon characterized by a degradation in performance. Such a violation, however, is ubiquitous in clinical settings where data are streamed temporally from different clinical sites and from a multitude of physiological sensors. To mitigate this interference, we propose a continual learning strategy, entitled CLOPS, that employs a replay buffer. To guide the storage of instances into the buffer, we propose end-to-end trainable parameters, termed task-instance parameters, that quantify the difficulty with which data points are classified by a deep-learning system. We validate the interpretation of these parameters via clinical domain knowledge. To replay instances from the buffer, we exploit uncertainty-based acquisition functions. In three of the four continual learning scenarios, reflecting transitions across diseases, time, data modalities, and healthcare institutions, we show that CLOPS outperforms the state-of-the-art methods, GEM1 and MIR2. We also conduct extensive ablation studies to demonstrate the necessity of the various components of our proposed strategy. Our framework has the potential to pave the way for diagnostic systems that remain robust over time.
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Affiliation(s)
- Dani Kiyasseh
- Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - David Clifton
- Department of Engineering Science, University of Oxford, Oxford, UK
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157
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Spatiotemporal effects of urban sprawl on habitat quality in the Pearl River Delta from 1990 to 2018. Sci Rep 2021; 11:13981. [PMID: 34234165 PMCID: PMC8263729 DOI: 10.1038/s41598-021-92916-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/16/2021] [Indexed: 02/06/2023] Open
Abstract
Since the implementation of the Chinese economic reforms. The habitat quality of coastal has gradually deteriorated with economic development, but the concept of "ecological construction" has slowed the negative trend. For quantitative analysis of the correlation between the Pearl River Delta urban expansion and changes in habitat quality under the influence of the policy, we first analyzed the habitat quality change based on the InVEST model and then measured the impact of construction land expansion on the habitat quality through habitat quality change index (HQCI) and contribution index (CI) indicators. Finally, the correlation between urbanization level and habitat quality was evaluated using geographically weighted regression (GWR) and the Self-organizing feature mapping neural network (SOFM). The results indicated that: (1) during the study period from 2000 to 2020, habitat quality declined due to urban sprawl, indicating a deterioration of ecological structure and function, and the decrease was most significant from 2000 to 2010. (2) The urbanization index had a negative effect on the habitat quality, but the negative effect have improved after 2000, reflecting the positive effect of policies such as "ecological civilization construction" (3) The implementation degree of ecological civilization varies greatly among cities in the study area: Shenzhen, Dongguan, Foshan, and Zhongshan have the best level of green development. These results reflect the positive role of policies in the prevention of damage to habitat quality caused by economic development and provide a reference for the formulation of sustainable urban development policies with spatial differences.
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158
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Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. COMPLEX INTELL SYST 2021; 7:2655-2678. [PMID: 34777970 PMCID: PMC8256231 DOI: 10.1007/s40747-021-00424-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/05/2021] [Indexed: 12/24/2022]
Abstract
A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.
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Affiliation(s)
- Onur Dogan
- Department of Industrial Engineering, Izmir Bakircay University, 35665 Izmir, Turkey
- Research Center for Data Analytics and Spatial Data Modeling (RC-DAS), Izmir Bakircay University, 35665 Izmir, Turkey
| | - Sanju Tiwari
- Department of Computer Science, Universidad Autonoma de Tamaulipas, Ciudad Victoria, Mexico
| | - M. A. Jabbar
- Vardhaman College of Engineering, Kacharam, India
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159
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Masum Bhuiyan MA, Mahmud S, Islam MR, Tasnim N. Volatility estimation for COVID-19 daily rates using Kalman filtering technique. RESULTS IN PHYSICS 2021; 26:104291. [PMID: 34026472 PMCID: PMC8130597 DOI: 10.1016/j.rinp.2021.104291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 04/30/2021] [Accepted: 05/01/2021] [Indexed: 06/12/2023]
Abstract
This paper discusses the use of stochastic modeling in the prognosis of Corona Virus-Infected Disease 2019 (COVID-19) cases. COVID-19 is a new disease that is highly infectious and dangerous. It has deeply shaken the world, claiming the lives of over a million people and bringing the world to a lockdown. So, the early detection of COVID is essential for the patients' timely treatment and preventive measures. A filtering technique with time-varying parameters is presented to predict the stochastic volatility (SV) of COVID-19 cases. The time-varying parameters are estimated using the Kalman filtering technique based on the stochastic component of data volatility. Kalman filtering is essential as it removes insignificant information from the data. We forecast one-step-ahead predicted volatility with ± 3 standard prediction errors, which is implemented by Maximum Likelihood Estimation. We conclude that Kalman filtering in conjunction with the SV model is a reliable predictive model for COVID-19 since it is less constrained by the past autoregressive information.
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160
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Shen J. Measuring the impact of mitigation measures on infection risk of covid-19 in Hong Kong since February 2020. CITIES (LONDON, ENGLAND) 2021; 114:103192. [PMID: 33776182 PMCID: PMC7985930 DOI: 10.1016/j.cities.2021.103192] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/22/2021] [Accepted: 03/18/2021] [Indexed: 05/05/2023]
Abstract
Is it possible to control the covid-19 pandemic in large cities like Hong Kong? Many cities have adopted various mitigation measures to contain the covid-19 pandemic. But few studies have been made to measure the impact of mitigation measures on infection risk at city level such as Hong Kong. This paper introduced three indicators to measure the infection risk of covid-19 under mitigation measures: the infection rate, the primary risk of infection and daily risk of infection. Two factors are introduced to consider the impact of mitigation measures on infection risk in Hong Kong. They are the number of trips per day and the percentage of people wearing face masks. With these two mitigation measures, the daily risk of infection was reduced from 1826.11 per million to 644.58 per million in the peak of covid-19 infection on 2 August 2020. The covid-19 infection risk would be 2.83 times higher if above mitigation measures were not adopted. The covid-19 pandemic continues in 2021 and city governments are strongly recommended to take effective measures to encourage the public to reduce unnecessary trips and wear face mask before the pandemic is fully controlled.
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Affiliation(s)
- Jianfa Shen
- Department of Geography and Resource Management, The Chinese University of Hong Kong SAR, China
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161
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Schultz B, Zaliani A, Ebeling C, Reinshagen J, Bojkova D, Lage-Rupprecht V, Karki R, Lukassen S, Gadiya Y, Ravindra NG, Das S, Baksi S, Domingo-Fernández D, Lentzen M, Strivens M, Raschka T, Cinatl J, DeLong LN, Gribbon P, Geisslinger G, Ciesek S, van Dijk D, Gardner S, Kodamullil AT, Fröhlich H, Peitsch M, Jacobs M, Hoeng J, Eils R, Claussen C, Hofmann-Apitius M. A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization. Sci Rep 2021; 11:11049. [PMID: 34040048 PMCID: PMC8155020 DOI: 10.1038/s41598-021-90296-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 05/04/2021] [Indexed: 02/08/2023] Open
Abstract
The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.
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Affiliation(s)
- Bruce Schultz
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Andrea Zaliani
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Christian Ebeling
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Jeanette Reinshagen
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Denisa Bojkova
- Institute for Medical Virology, University Hospital Frankfurt, 60590, Frankfurt am Main, Germany
| | - Vanessa Lage-Rupprecht
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Reagon Karki
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Sören Lukassen
- Center for Digital Health, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Yojana Gadiya
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Neal G Ravindra
- Center for Biomedical Data Science, Yale School of Medicine, Yale University, 333 Cedar Street, New Haven, CT, 06510, USA
| | - Sayoni Das
- Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ, UK
| | - Shounak Baksi
- Causality BioModels Pvt Ltd., Kinfra Hi-Tech Park, Kerala Technology Innovation Zone- KTIZ, Kalamassery, Cochin, 683503, India
| | - Daniel Domingo-Fernández
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Manuel Lentzen
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Mark Strivens
- Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ, UK
| | - Tamara Raschka
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Jindrich Cinatl
- Institute for Medical Virology, University Hospital Frankfurt, 60590, Frankfurt am Main, Germany
| | - Lauren Nicole DeLong
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Phil Gribbon
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Gerd Geisslinger
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
- Pharmazentrum Frankfurt/ZAFES, Institut Für Klinische Pharmakologie, Klinikum Der Goethe-Universität Frankfurt, 60590, Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 60596, Frankfurt am Main, Germany
| | - Sandra Ciesek
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 60596, Frankfurt am Main, Germany
- Institute for Medical Virology, University Hospital Frankfurt, 60590, Frankfurt am Main, Germany
- DZIF, German Centre for Infection Research, External Partner Site, 60596, Frankfurt am Main, Germany
| | - David van Dijk
- Center for Biomedical Data Science, Yale School of Medicine, Yale University, 333 Cedar Street, New Haven, CT, 06510, USA
| | - Steve Gardner
- Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ, UK
| | - Alpha Tom Kodamullil
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Manuel Peitsch
- Philipp Morris International R&D, Biological Systems Research, R&D Innovation Cube T1517.07, Quai Jeanrenaud 5, 2000, Neuchâte, Switzerland
| | - Marc Jacobs
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Julia Hoeng
- Philipp Morris International R&D, Biological Systems Research, R&D Innovation Cube T1517.07, Quai Jeanrenaud 5, 2000, Neuchâte, Switzerland
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Claussen
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany.
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162
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Restar AJ, Garrison-Desany HM, Adamson T, Childress C, Millett G, Jarrett BA, Howell S, Glick JL, Beckham SW, Baral S. HIV treatment engagement in the context of COVID-19: an observational global sample of transgender and nonbinary people living with HIV. BMC Public Health 2021; 21:901. [PMID: 33980193 PMCID: PMC8114659 DOI: 10.1186/s12889-021-10977-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 04/29/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND HIV services, like many medical services, have been disrupted by the COVID-19 pandemic. However, there are limited data on the impacts of the COVID-19 pandemic on HIV treatment engagement outcomes among transgender (trans) and nonbinary people. This study addresses a pressing knowledge gap and is important in its global scope, its use of technology for recruitment, and focus on transgender people living with HIV. The objective of this study is to examine correlates of HIV infection and HIV treatment engagement outcomes (i.e., currently on ART, having an HIV provider, having access to HIV treatment without burden, and remote refills) since the COVID-19 pandemic began. METHODS We utilized observational data from the Global COVID-19 Disparities Survey 2020, an online study that globally sampled trans and nonbinary people (n = 902) between April and August 2020. We conducted a series of multivariable logistic regressions with lasso selection to explore correlates of HIV treatment engagement outcomes in the context of COVID-19. RESULTS Of the 120 (13.3%) trans and nonbinary people living with HIV in this survey, the majority (85.8%) were currently on HIV treatment. A smaller proportion (69.2%) reported having access to an HIV provider since COVID-19 control measures were implemented. Less than half reported being able to access treatment without burdens related to COVID-19 (48.3%) and having the ability to remotely refill HIV prescription (44.2%). After adjusting for gender in the multivariable models, younger age and anticipated job loss were significantly associated with not having access to HIV treatment without burden. Outcomes also significantly varied by geographic region, with respondents reporting less access to an HIV provider in nearly every region outside of South-East Asia. CONCLUSION Our results suggest that currently taking ART, having access to an HIV provider, and being able to access HIV treatment without burden and remotely refill HIV medication are suboptimal among trans and nonbinary people living with HIV across the world. Strengthening support for HIV programs that are well-connected to trans and nonbinary communities, increasing remote access to HIV providers and prescription refills, and providing socioeconomic support could significantly improve HIV engagement in trans and nonbinary communities.
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Affiliation(s)
- Arjee Javellana Restar
- Department of Epidemiology, Johns Hopkins School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA.
| | - Henri M Garrison-Desany
- Department of Epidemiology, Johns Hopkins School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA
| | - Tyler Adamson
- Department of Health, Policy, and Management, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Chase Childress
- School of Law and School of Criminology and Criminal Justice, Northeastern University, Boston, MA, USA
| | | | - Brooke A Jarrett
- Department of Epidemiology, Johns Hopkins School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA
| | | | - Jennifer L Glick
- Department of Health, Behavior, and Society, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - S Wilson Beckham
- Department of Health, Behavior, and Society, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of International Health, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Stefan Baral
- Department of Epidemiology, Johns Hopkins School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA
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163
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Mahdi A, Błaszczyk P, Dłotko P, Salvi D, Chan TS, Harvey J, Gurnari D, Wu Y, Farhat A, Hellmer N, Zarebski A, Hogan B, Tarassenko L. OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19. Sci Rep 2021; 11:9237. [PMID: 33927237 PMCID: PMC8084933 DOI: 10.1038/s41598-021-88481-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 03/30/2021] [Indexed: 11/28/2022] Open
Abstract
Oxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level, and for more than 50 countries at the regional level. It is curated from a variety of (wherever available) official sources. Its purpose is to facilitate the analysis of the spread of SARS-CoV-2 virus and to assess the effects of non-pharmaceutical interventions to reduce the impact of the pandemic. Our database is a freely available, daily updated tool that provides unified and granular information across geographical regions. Design type Data integration objective Measurement(s) Coronavirus infectious disease, viral epidemiology Technology type(s) Digital curation Factor types(s) Sample characteristic(s) Homo sapiens.
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Affiliation(s)
- Adam Mahdi
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
| | - Piotr Błaszczyk
- Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Krakow, Poland
| | - Paweł Dłotko
- Dioscuri Centre in Topological Data Analysis, Mathematical Institute, Polish Academy of Sciences, Warsaw, Poland
- Department of Mathematics, Swansea University, Swansea, UK
| | - Dario Salvi
- School of Arts and Communication (K3), Malmö University, Malmö, Sweden
| | - Tak-Shing Chan
- Department of Mathematics, Swansea University, Swansea, UK
| | - John Harvey
- Department of Mathematics, Swansea University, Swansea, UK
| | - Davide Gurnari
- Department of Mathematics, University of Padova, Padova, Italy
| | - Yue Wu
- Mathematical Institute, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Ahmad Farhat
- American University of Sharjah, Sharjah, United Arab Emirates
| | - Niklas Hellmer
- Department of Mathematics, Swansea University, Swansea, UK
| | | | - Bernie Hogan
- Oxford Internet Institute, University of Oxford, Oxford, UK
| | - Lionel Tarassenko
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
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164
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Negligible impacts of early COVID-19 confinement on household carbon footprints in Japan. ONE EARTH (CAMBRIDGE, MASS.) 2021; 4:553-564. [PMID: 35497090 PMCID: PMC9033312 DOI: 10.1016/j.oneear.2021.03.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 03/01/2021] [Accepted: 03/24/2021] [Indexed: 12/23/2022]
Abstract
The rapid and extensive changes in household consumption patterns during the coronavirus disease 2019 (COVID-19) pandemic can serve as a natural experiment for exploring the environmental outcomes of changing human behavior. Here, we assess the carbon footprint of household consumption in Japan during the early stages of the COVID-19 pandemic (January–May 2020), which were characterized by moderate confinement measures. The associated lifestyle changes did not have a significant effect on the overall household carbon footprint compared with 2015–2019 levels. However, there were significant trade-offs between individual consumption categories such that the carbon footprint increased for some categories (e.g., eating at home) or declined (e.g., eating out, transportation, clothing, and entertainment) or remained relatively unchanged (e.g., housing) for others. Furthermore, carbon footprint patterns between age groups were largely consistent with 2015–2019 levels. However, changes in food-related carbon footprints were visible for all age groups since March and, in some cases, since February. Households are major sources of greenhouse gase (GHG) emissions both directly through energy use for transport, heating, and other activities and indirectly through emissions embedded in the goods and services they consume. Changes in lifestyles and consumption patterns can have major ramifications for GHG emissions. The COVID-19 pandemic catalyzed profound and rapid lifestyle shifts, which makes it a natural experiment for studying the outcomes of such changes for GHG emissions. Despite shifts in the work, socialization, and consumption practices of Japanese households during the early stages of the pandemic (January–May 2020), the overall changes in carbon footprints were negligible. Despite some trade-offs between consumption categories, the general carbon footprint patterns remained similar to 2015–2019 trends and are consistent among age groups. This has implications for decarbonization efforts in that the environmental benefits of changes in consumption patterns might not materialize automatically and be easily reversible.
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165
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Projected incremental changes to extreme wind-driven wave heights for the twenty-first century. Sci Rep 2021; 11:8826. [PMID: 33893340 PMCID: PMC8065105 DOI: 10.1038/s41598-021-87358-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 03/19/2021] [Indexed: 12/05/2022] Open
Abstract
Global climate change will alter wind sea and swell waves, modifying the severity, frequency and impact of episodic coastal flooding and morphological change. Global-scale estimates of increases to coastal impacts have been typically attributed to sea level rise and not specifically to changes to waves on their own. This study provides a reduced complexity method for applying projected extreme wave changes to local scale impact studies. We use non-stationary extreme value analysis to distil an incremental change signal in extreme wave heights and associate this with a change in the frequency of events globally. Extreme wave heights are not projected to increase everywhere. We find that the largest increases will typically be experienced at higher latitudes, and that there is high ensemble model agreement on an increase (doubling of events) for the waters south of Australia, the Arabian Sea and the Gulf of Guinea by the end of the twenty-first century.
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166
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Khavari B, Korkovelos A, Sahlberg A, Howells M, Fuso Nerini F. Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa. Sci Data 2021; 8:117. [PMID: 33893317 PMCID: PMC8065116 DOI: 10.1038/s41597-021-00897-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 03/18/2021] [Indexed: 02/02/2023] Open
Abstract
Human settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response.
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Affiliation(s)
- Babak Khavari
- Division of Energy Systems, KTH Royal Institute of Technology, Brinellvägen 68, 10044, Stockholm, Sweden.
| | - Alexandros Korkovelos
- Division of Energy Systems, KTH Royal Institute of Technology, Brinellvägen 68, 10044, Stockholm, Sweden
- The World Bank Group, Washington, DC, 20433, USA
| | - Andreas Sahlberg
- Division of Energy Systems, KTH Royal Institute of Technology, Brinellvägen 68, 10044, Stockholm, Sweden
| | - Mark Howells
- Department of Geography and Environment, Loughborough University, Leicestershire, LE11 3TU, UK
- Center for Environmental Policy, Imperial College, London, SW7 1NE, UK
| | - Francesco Fuso Nerini
- Division of Energy Systems, KTH Royal Institute of Technology, Brinellvägen 68, 10044, Stockholm, Sweden
- RFF-CMCC European Institute on Economics and the Environment, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, 20143, Milano, Italy
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167
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Zhu D, Ye X, Manson S. Revealing the spatial shifting pattern of COVID-19 pandemic in the United States. Sci Rep 2021; 11:8396. [PMID: 33875751 PMCID: PMC8055907 DOI: 10.1038/s41598-021-87902-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 04/06/2021] [Indexed: 11/09/2022] Open
Abstract
We describe the use of network modeling to capture the shifting spatiotemporal nature of the COVID-19 pandemic. The most common approach to tracking COVID-19 cases over time and space is to examine a series of maps that provide snapshots of the pandemic. A series of snapshots can convey the spatial nature of cases but often rely on subjective interpretation to assess how the pandemic is shifting in severity through time and space. We present a novel application of network optimization to a standard series of snapshots to better reveal how the spatial centres of the pandemic shifted spatially over time in the mainland United States under a mix of interventions. We find a global spatial shifting pattern with stable pandemic centres and both local and long-range interactions. Metrics derived from the daily nature of spatial shifts are introduced to help evaluate the pandemic situation at regional scales. We also highlight the value of reviewing pandemics through local spatial shifts to uncover dynamic relationships among and within regions, such as spillover and concentration among states. This new way of examining the COVID-19 pandemic in terms of network-based spatial shifts offers new story lines in understanding how the pandemic spread in geography.
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Affiliation(s)
- Di Zhu
- Department of Geography, Environment and Society, University of Minnesota, Twin Cities, USA.
- Beijing Key Lab of Spatial Information Integration and Its Applications, Peking University, Beijing, China.
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, USA
| | - Steven Manson
- Department of Geography, Environment and Society, University of Minnesota, Twin Cities, USA
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168
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Ahmed S, Wang D, Park J, Cho SH. UWB-gestures, a public dataset of dynamic hand gestures acquired using impulse radar sensors. Sci Data 2021; 8:102. [PMID: 33846358 PMCID: PMC8041886 DOI: 10.1038/s41597-021-00876-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 02/19/2021] [Indexed: 11/08/2022] Open
Abstract
In the past few decades, deep learning algorithms have become more prevalent for signal detection and classification. To design machine learning algorithms, however, an adequate dataset is required. Motivated by the existence of several open-source camera-based hand gesture datasets, this descriptor presents UWB-Gestures, the first public dataset of twelve dynamic hand gestures acquired with ultra-wideband (UWB) impulse radars. The dataset contains a total of 9,600 samples gathered from eight different human volunteers. UWB-Gestures eliminates the need to employ UWB radar hardware to train and test the algorithm. Additionally, the dataset can provide a competitive environment for the research community to compare the accuracy of different hand gesture recognition (HGR) algorithms, enabling the provision of reproducible research results in the field of HGR through UWB radars. Three radars were placed at three different locations to acquire the data, and the respective data were saved independently for flexibility.
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Affiliation(s)
- Shahzad Ahmed
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Dingyang Wang
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Junyoung Park
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Sung Ho Cho
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea.
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169
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Raza A, Tabassum J, Kudapa H, Varshney RK. Can omics deliver temperature resilient ready-to-grow crops? Crit Rev Biotechnol 2021; 41:1209-1232. [PMID: 33827346 DOI: 10.1080/07388551.2021.1898332] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Plants are extensively well-thought-out as the main source for nourishing natural life on earth. In the natural environment, plants have to face several stresses, mainly heat stress (HS), chilling stress (CS) and freezing stress (FS) due to adverse climate fluctuations. These stresses are considered as a major threat for sustainable agriculture by hindering plant growth and development, causing damage, ultimately leading to yield losses worldwide and counteracting to achieve the goal of "zero hunger" proposed by the Food and Agricultural Organization (FAO) of the United Nations. Notably, this is primarily because of the numerous inequities happening at the cellular, molecular and/or physiological levels, especially during plant developmental stages under temperature stress. Plants counter to temperature stress via a complex phenomenon including variations at different developmental stages that comprise modifications in physiological and biochemical processes, gene expression and differences in the levels of metabolites and proteins. During the last decade, omics approaches have revolutionized how plant biologists explore stress-responsive mechanisms and pathways, driven by current scientific developments. However, investigations are still required to explore numerous features of temperature stress responses in plants to create a complete idea in the arena of stress signaling. Therefore, this review highlights the recent advances in the utilization of omics approaches to understand stress adaptation and tolerance mechanisms. Additionally, how to overcome persisting knowledge gaps. Shortly, the combination of integrated omics, genome editing, and speed breeding can revolutionize modern agricultural production to feed millions worldwide in order to accomplish the goal of "zero hunger."
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Affiliation(s)
- Ali Raza
- Key Lab of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Wuhan, China
| | - Javaria Tabassum
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Science (CAAS), Hangzhou, China
| | - Himabindu Kudapa
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.,The UWA Institute of Agriculture, The University of Western Australia, Perth, Australia
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170
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Huveneers C, Jaine FRA, Barnett A, Butcher PA, Clarke TM, Currey-Randall LM, Dwyer RG, Ferreira LC, Gleiss AC, Hoenner X, Ierodiaconou D, Lédée EJI, Meekan MG, Pederson H, Rizzari JR, van Ruth PD, Semmens JM, Taylor MD, Udyawer V, Walsh P, Heupel MR, Harcourt R. The power of national acoustic tracking networks to assess the impacts of human activity on marine organisms during the COVID-19 pandemic. BIOLOGICAL CONSERVATION 2021; 256:108995. [PMID: 34580542 PMCID: PMC8457752 DOI: 10.1016/j.biocon.2021.108995] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 12/22/2020] [Accepted: 01/16/2021] [Indexed: 05/16/2023]
Abstract
COVID-19 restrictions have led to an unprecedented global hiatus in anthropogenic activities, providing a unique opportunity to assess human impact on biological systems. Here, we describe how a national network of acoustic tracking receivers can be leveraged to assess the effects of human activity on animal movement and space use during such global disruptions. We outline variation in restrictions on human activity across Australian states and describe four mechanisms affecting human interactions with the marine environment: 1) reduction in economy and trade changing shipping traffic; 2) changes in export markets affecting commercial fisheries; 3) alterations in recreational activities; and 4) decline in tourism. We develop a roadmap for the analysis of acoustic tracking data across various scales using Australia's national Integrated Marine Observing System (IMOS) Animal Tracking Facility as a case study. We illustrate the benefit of sustained observing systems and monitoring programs by assessing how a 51-day break in white shark (Carcharodon carcharias) cage-diving tourism due to COVID-19 restrictions affected the behaviour and space use of two resident species. This cessation of tourism activities represents the longest break since cage-diving vessels started day trips in this area in 2007. Long-term monitoring of the local environment reveals that the activity space of yellowtail kingfish (Seriola lalandi) was reduced when cage-diving boats were absent compared to periods following standard tourism operations. However, white shark residency and movements were not affected. Our roadmap is globally applicable and will assist researchers in designing studies to assess how anthropogenic activities can impact animal movement and distributions during regional, short-term through to major, unexpected disruptions like the COVID-19 pandemic.
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Affiliation(s)
- Charlie Huveneers
- Southern Shark Ecology Group, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | - Fabrice R A Jaine
- Integrated Marine Observing System (IMOS) Animal Tracking Facility, Sydney Institute of Marine Science, Mosman, NSW 2088, Australia
- Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
| | - Adam Barnett
- College of Science & Engineering James Cook University, Cairns, QLD, 4878, Australia
| | - Paul A Butcher
- NSW Department of Primary Industries, National Marine Science Centre, Coffs Harbour, NSW 2450, Australia
| | - Thomas M Clarke
- Southern Shark Ecology Group, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | | | - Ross G Dwyer
- Global Change Ecology Research Group, University of the Sunshine Coast, Maroochydore, QLD, Australia
| | | | - Adrian C Gleiss
- College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA 6150, Australia
- Centre for Sustainable Aquatic Ecosystems, Harry Butler Institute, Murdoch University, Murdoch, WA 6150, Australia
| | - Xavier Hoenner
- CSIRO Oceans and Atmosphere, CSIRO, Hobart, TAS 7000, Australia
| | - Daniel Ierodiaconou
- School of Life and Environmental Sciences, Centre for Integrative Ecology, Deakin University, Warrnambool, VIC 3280, Australia
| | - Elodie J I Lédée
- Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Mark G Meekan
- Australian Institute of Marine Science, Perth, WA 6009, Australia
| | | | - Justin R Rizzari
- School of Life and Environmental Sciences, Deakin University, Queenscliff, VIC, 3225, Australia
| | - Paul D van Ruth
- South Australian Research and Development Institute - Aquatic Sciences, West Beach, SA 5024, Australia
| | - Jayson M Semmens
- Fisheries and Aquaculture Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 49, Hobart, TAS 7001, Australia
| | - Matthew D Taylor
- Port Stephens Fisheries Institute, New South Wales Department of Primary Industries, Locked Bag 1, Nelson Bay, NSW 2315, Australia
| | - Vinay Udyawer
- Australian Institute of Marine Science, Darwin, NT 0810, Australia
| | - Peter Walsh
- Fisheries and Aquaculture Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 49, Hobart, TAS 7001, Australia
| | - Michelle R Heupel
- Integrated Marine Observing System (IMOS), University of Tasmania, Hobart, TAS 7000, Australia
| | - Robert Harcourt
- Integrated Marine Observing System (IMOS) Animal Tracking Facility, Sydney Institute of Marine Science, Mosman, NSW 2088, Australia
- Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
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Opgenoorth L, Dauphin B, Benavides R, Heer K, Alizoti P, Martínez-Sancho E, Alía R, Ambrosio O, Audrey A, Auñón F, Avanzi C, Avramidou E, Bagnoli F, Barbas E, Bastias CC, Bastien C, Ballesteros E, Beffa G, Bernier F, Bignalet H, Bodineau G, Bouic D, Brodbeck S, Brunetto W, Buchovska J, Buy M, Cabanillas-Saldaña AM, Carvalho B, Cheval N, Climent JM, Correard M, Cremer E, Danusevičius D, Del Caño F, Denou JL, di Gerardi N, Dokhelar B, Ducousso A, Eskild Nilsen A, Farsakoglou AM, Fonti P, Ganopoulos I, García Del Barrio JM, Gilg O, González-Martínez SC, Graf R, Gray A, Grivet D, Gugerli F, Hartleitner C, Hollenbach E, Hurel A, Issehut B, Jean F, Jorge V, Jouineau A, Kappner JP, Kärkkäinen K, Kesälahti R, Knutzen F, Kujala ST, Kumpula TA, Labriola M, Lalanne C, Lambertz J, Lascoux M, Lejeune V, Le-Provost G, Levillain J, Liesebach M, López-Quiroga D, Meier B, Malliarou E, Marchon J, Mariotte N, Mas A, Matesanz S, Meischner H, Michotey C, Milesi P, Morganti S, Nievergelt D, Notivol E, Ostreng G, Pakull B, Perry A, Piotti A, Plomion C, Poinot N, Pringarbe M, Puzos L, Pyhäjärvi T, Raffin A, Ramírez-Valiente JA, Rellstab C, Remi D, Richter S, Robledo-Arnuncio JJ, San Segundo S, Savolainen O, Schueler S, Schneck V, Scotti I, Semerikov V, Slámová L, Sønstebø JH, Spanu I, Thevenet J, Tollefsrud MM, Turion N, Vendramin GG, Villar M, von Arx G, Westin J, Fady B, Myking T, Valladares F, Aravanopoulos FA, Cavers S. The GenTree Platform: growth traits and tree-level environmental data in 12 European forest tree species. Gigascience 2021; 10:6177710. [PMID: 33734368 PMCID: PMC7970660 DOI: 10.1093/gigascience/giab010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 12/07/2020] [Accepted: 02/03/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Progress in the field of evolutionary forest ecology has been hampered by the huge challenge of phenotyping trees across their ranges in their natural environments, and the limitation in high-resolution environmental information. FINDINGS The GenTree Platform contains phenotypic and environmental data from 4,959 trees from 12 ecologically and economically important European forest tree species: Abies alba Mill. (silver fir), Betula pendula Roth. (silver birch), Fagus sylvatica L. (European beech), Picea abies (L.) H. Karst (Norway spruce), Pinus cembra L. (Swiss stone pine), Pinus halepensis Mill. (Aleppo pine), Pinus nigra Arnold (European black pine), Pinus pinaster Aiton (maritime pine), Pinus sylvestris L. (Scots pine), Populus nigra L. (European black poplar), Taxus baccata L. (English yew), and Quercus petraea (Matt.) Liebl. (sessile oak). Phenotypic (height, diameter at breast height, crown size, bark thickness, biomass, straightness, forking, branch angle, fructification), regeneration, environmental in situ measurements (soil depth, vegetation cover, competition indices), and environmental modeling data extracted by using bilinear interpolation accounting for surrounding conditions of each tree (precipitation, temperature, insolation, drought indices) were obtained from trees in 194 sites covering the species' geographic ranges and reflecting local environmental gradients. CONCLUSION The GenTree Platform is a new resource for investigating ecological and evolutionary processes in forest trees. The coherent phenotyping and environmental characterization across 12 species in their European ranges allow for a wide range of analyses from forest ecologists, conservationists, and macro-ecologists. Also, the data here presented can be linked to the GenTree Dendroecological collection, the GenTree Leaf Trait collection, and the GenTree Genomic collection presented elsewhere, which together build the largest evolutionary forest ecology data collection available.
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Affiliation(s)
- Lars Opgenoorth
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany.,Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Benjamin Dauphin
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Raquel Benavides
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Katrin Heer
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Paraskevi Alizoti
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | | | - Ricardo Alía
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Olivier Ambrosio
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Albet Audrey
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Francisco Auñón
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Camilla Avanzi
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Evangelia Avramidou
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Francesca Bagnoli
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Evangelos Barbas
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Cristina C Bastias
- Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), CNRS, UMR 5175, 34090, Montpellier, France
| | - Catherine Bastien
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Dept ECOFA, 45075, Orléans, France
| | - Eduardo Ballesteros
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Giorgia Beffa
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Frédéric Bernier
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Henri Bignalet
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Guillaume Bodineau
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), GBFOR, 45075, Orléans, France
| | - Damien Bouic
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Sabine Brodbeck
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - William Brunetto
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Jurata Buchovska
- Vytautas Magnus University, Studentu Street 11, 53361, Akademija, Lithuania
| | - Melanie Buy
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Ana M Cabanillas-Saldaña
- Departamento de Agricultura, Ganadería y Medio Ambiente, Gobierno de Aragón, P. Mª Agustín 36, 50071, Zaragoza, Spain
| | - Bárbara Carvalho
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Nicolas Cheval
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - José M Climent
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Marianne Correard
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Eva Cremer
- Bavarian Institute for Forest Genetics, Forstamtsplatz 1, 83317, Teisendorf, Germany
| | | | - Fernando Del Caño
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Jean-Luc Denou
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Nicolas di Gerardi
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Bernard Dokhelar
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | | | - Anne Eskild Nilsen
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Anna-Maria Farsakoglou
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Patrick Fonti
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Ioannis Ganopoulos
- Institute of Plant Breeding and Genetic Resources, Hellenic Agricultural Organization DEMETER (ex NAGREF), 57001, Thermi, Greece
| | - José M García Del Barrio
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Olivier Gilg
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | | | - René Graf
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Alan Gray
- UK Centre for Ecology and Hydrology, Bush Estate Penicuik, EH26 0QB, Edinburgh, UK
| | - Delphine Grivet
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Felix Gugerli
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | | | - Enja Hollenbach
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Agathe Hurel
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Bernard Issehut
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Florence Jean
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Veronique Jorge
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), ONF, BIOFORA, 45075, Orléans, France
| | - Arnaud Jouineau
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Jan-Philipp Kappner
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Katri Kärkkäinen
- Natural Resources Institute Finland, Paavo Havaksentie 3, 90014, University of Oulu, Finland
| | - Robert Kesälahti
- University of Oulu, Pentti Kaiteran katu 1, 90014, University of Oulu, Finland
| | - Florian Knutzen
- Bavarian Institute for Forest Genetics, Forstamtsplatz 1, 83317, Teisendorf, Germany
| | - Sonja T Kujala
- Natural Resources Institute Finland, Paavo Havaksentie 3, 90014, University of Oulu, Finland
| | - Timo A Kumpula
- University of Oulu, Pentti Kaiteran katu 1, 90014, University of Oulu, Finland
| | - Mariaceleste Labriola
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Celine Lalanne
- INRAE, Univsité de Bordeaux, BIOGECO, 33770, Cestas, France
| | - Johannes Lambertz
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Martin Lascoux
- Department of Ecology & Genetics, EBC, Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
| | - Vincent Lejeune
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), GBFOR, 45075, Orléans, France
| | | | - Joseph Levillain
- Université de Lorraine, AgroParisTech, INRAE, SILVA, 54000, Nancy, France
| | - Mirko Liesebach
- Thünen Institute of Forest Genetics, Sieker Landstr. 2, 22927, Grosshansdorf, Germany
| | - David López-Quiroga
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Benjamin Meier
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Ermioni Malliarou
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Jérémy Marchon
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Nicolas Mariotte
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Antonio Mas
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Silvia Matesanz
- Área de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Calle Tulipán s/n, 28933, Móstoles, Spain
| | - Helge Meischner
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Célia Michotey
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), URGI, Versailles, France
| | - Pascal Milesi
- Department of Ecology & Genetics, EBC, Science for Life Laboratory, Uppsala University, 75236, Uppsala, Sweden
| | - Sandro Morganti
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Daniel Nievergelt
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Eduardo Notivol
- Centro de Investigación y Tecnología Agroalimentaria de Aragón - Unidad de Recursos Forestales (CITA), Avda. Montañana 930, 50059, Zaragoza, Spain
| | - Geir Ostreng
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Birte Pakull
- Thünen Institute of Forest Genetics, Sieker Landstr. 2, 22927, Grosshansdorf, Germany
| | - Annika Perry
- UK Centre for Ecology and Hydrology, Bush Estate Penicuik, EH26 0QB, Edinburgh, UK
| | - Andrea Piotti
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | | | - Nicolas Poinot
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Mehdi Pringarbe
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Luc Puzos
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Tanja Pyhäjärvi
- University of Oulu, Pentti Kaiteran katu 1, 90014, University of Oulu, Finland
| | - Annie Raffin
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - José A Ramírez-Valiente
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Christian Rellstab
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Dourthe Remi
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Sebastian Richter
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Juan J Robledo-Arnuncio
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Sergio San Segundo
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Outi Savolainen
- University of Oulu, Pentti Kaiteran katu 1, 90014, University of Oulu, Finland
| | - Silvio Schueler
- Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1131, Wien, Austria
| | - Volker Schneck
- Thünen Institute of Forest Genetics, Eberswalder Chaussee 3a, 15377, Waldsieversdorf, Germany
| | - Ivan Scotti
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Vladimir Semerikov
- Institute of Plant and Animal Ecology, Ural branch of RAS, 8 Marta St. 202, 620144, Ekaterinburg, Russia
| | - Lenka Slámová
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Jørn Henrik Sønstebø
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Ilaria Spanu
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Jean Thevenet
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Mari Mette Tollefsrud
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Norbert Turion
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Giovanni Giuseppe Vendramin
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Marc Villar
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), ONF, BIOFORA, 45075, Orléans, France
| | - Georg von Arx
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | | | - Bruno Fady
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Tor Myking
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Fernando Valladares
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Filippos A Aravanopoulos
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Stephen Cavers
- UK Centre for Ecology and Hydrology, Bush Estate Penicuik, EH26 0QB, Edinburgh, UK
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Ma X, Jin J, Zhu L, Liu J. Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau. PeerJ 2021; 9:e11040. [PMID: 33777529 PMCID: PMC7977383 DOI: 10.7717/peerj.11040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 02/09/2021] [Indexed: 11/24/2022] Open
Abstract
This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0-10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.
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Affiliation(s)
- Xiaogang Ma
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F Universtiy, Yangling, Shaanxi Province, China
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi Province, China
| | - Jiming Jin
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F Universtiy, Yangling, Shaanxi Province, China
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi Province, China
| | - Lingjing Zhu
- South China Sea Institute of Marine Meteorology & College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, Guangdong, China
- Southern Marine Science and Engineering Guangdong Laboratory, Zhanjiang, Guangdong, China
| | - Jian Liu
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F Universtiy, Yangling, Shaanxi Province, China
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi Province, China
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Chakrabarty RK, Beeler P, Liu P, Goswami S, Harvey RD, Pervez S, van Donkelaar A, Martin RV. Ambient PM 2.5 exposure and rapid spread of COVID-19 in the United States. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 760:143391. [PMID: 33250247 PMCID: PMC7651233 DOI: 10.1016/j.scitotenv.2020.143391] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 05/12/2023]
Abstract
It has been posited that populations being exposed to long-term air pollution are more susceptible to COVID-19. Evidence is emerging that long-term exposure to ambient PM2.5 (particulate matter with aerodynamic diameter 2.5 μm or less) associates with higher COVID-19 mortality rates, but whether it also associates with the speed at which the disease is capable of spreading in a population is unknown. Here, we establish the association between long-term exposure to ambient PM2.5 in the United States (US) and COVID-19 basic reproduction ratio R0- a dimensionless epidemic measure of the rapidity of disease spread through a population. We inferred state-level R0 values using a state-of-the-art susceptible, exposed, infected, and recovered (SEIR) model initialized with COVID-19 epidemiological data corresponding to the period March 2-April 30. This period was characterized by a rapid surge in COVID-19 cases across the US states, implementation of strict social distancing measures, and a significant drop in outdoor air pollution. We find that an increase of 1 μg/m3 in PM2.5 levels below current national ambient air quality standards associates with an increase of 0.25 in R0 (95% CI: 0.048-0.447). A 10% increase in secondary inorganic composition, sulfate-nitrate-ammonium, in PM2.5 associates with ≈10% increase in R0 by 0.22 (95% CI: 0.083-0.352), and presence of black carbon (soot) in the ambient environment moderates this relationship. We considered several potential confounding factors in our analysis, including gaseous air pollutants and socio-economical and meteorological conditions. Our results underscore two policy implications - first, regulatory standards need to be better guided by exploring the concentration-response relationships near the lower end of the PM2.5 air quality distribution; and second, pollution regulations need to be continually enforced for combustion emissions that largely determine secondary inorganic aerosol formation.
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Affiliation(s)
- Rajan K Chakrabarty
- Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Institute for Public Health, Washington University in St. Louis, St. Louis, MO 63130, USA.
| | - Payton Beeler
- Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Pai Liu
- Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Spondita Goswami
- Experimental Psychology Program, Department of Psychology, Saint Louis University, St. Louis, MO 63108, USA
| | - Richard D Harvey
- Experimental Psychology Program, Department of Psychology, Saint Louis University, St. Louis, MO 63108, USA
| | - Shamsh Pervez
- School of Studies in Chemistry, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh 492010, India
| | - Aaron van Donkelaar
- Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Randall V Martin
- Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
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174
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Analyzing knowledge entities about COVID-19 using entitymetrics. Scientometrics 2021; 126:4491-4509. [PMID: 33746309 PMCID: PMC7953944 DOI: 10.1007/s11192-021-03933-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/26/2021] [Indexed: 12/24/2022]
Abstract
COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity–entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.
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175
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Wei Y, Wang J, Song W, Xiu C, Ma L, Pei T. Spread of COVID-19 in China: analysis from a city-based epidemic and mobility model. CITIES (LONDON, ENGLAND) 2021; 110:103010. [PMID: 33162634 PMCID: PMC7598765 DOI: 10.1016/j.cities.2020.103010] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 09/09/2020] [Accepted: 10/24/2020] [Indexed: 05/04/2023]
Abstract
Understanding the processes and mechanisms of the spatial spread of epidemics is essential for making reasonable judgments on the development trends of epidemics and for adopting effective containment measures. Using multi-agent network technology and big data on population migration, this paper constructed a city-based epidemic and mobility model (CEMM) to stimulate the spatiotemporal of COVID-19. Compared with traditional models, this model is characterized by an urban network perspective and emphasizes the important role of intercity population mobility and high-speed transportation networks. The results show that the model could simulate the inter-city spread of COVID-19 at the early stage in China with high precision. Through scenario simulation, the paper quantitatively evaluated the effect of control measures "city lockdown" and "decreasing population mobility" on containing the spatial spread of the COVID-19 epidemic. According to the simulation, the total number of infectious cases in China would have climbed to 138,824 on February 2020, or 4.46 times the real number, if neither of the measures had been implemented. Overall, the containment effect of the lockdown of cities in Hubei was greater than that of decreasing intercity population mobility, and the effect of city lockdowns was more sensitive to timing relative to decreasing population mobility.
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Affiliation(s)
- Ye Wei
- Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, Jilin 130024, China
| | - Jiaoe Wang
- Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Song
- Department of Geography and Geosciences, University of Louisville, Louisville, KY, USA
| | - Chunliang Xiu
- College of Jang Ho Architecture, Northeastern University, Shenyang, Liaoning, China
| | - Li Ma
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Pei
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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176
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Li Y, Sun P, Lu Z, Chen J, Wang Z, Du X, Zheng Z, Wu Y, Hu H, Yang J, Ma J, Liu J, Yang Y. The Corylus mandshurica genome provides insights into the evolution of Betulaceae genomes and hazelnut breeding. HORTICULTURE RESEARCH 2021; 8:54. [PMID: 33642584 PMCID: PMC7917096 DOI: 10.1038/s41438-021-00495-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 01/11/2021] [Accepted: 01/20/2021] [Indexed: 05/04/2023]
Abstract
Hazelnut is popular for its flavor, and it has also been suggested that hazelnut is beneficial to cardiovascular health because it is rich in oleic acid. Here, we report the first high-quality chromosome-scale genome for the hazelnut species Corylus mandshurica (2n = 22), which has a high concentration of oleic acid in its nuts. The assembled genome is 367.67 Mb in length, and the contig N50 is 14.85 Mb. All contigs were assembled into 11 chromosomes, and 28,409 protein-coding genes were annotated. We reconstructed the evolutionary trajectories of the genomes of Betulaceae species and revealed that the 11 chromosomes of the hazelnut genus were derived from the most ancestral karyotype in Betula pendula, which has 14 protochromosomes, by inferring homology among five Betulaceae genomes. We identified 96 candidate genes involved in oleic acid biosynthesis, and 10 showed rapid evolution or positive selection. These findings will help us to understand the mechanisms of lipid synthesis and storage in hazelnuts. Several gene families related to salicylic acid metabolism and stress responses experienced rapid expansion in this hazelnut species, which may have increased its stress tolerance. The reference genome presented here constitutes a valuable resource for molecular breeding and genetic improvement of the important agronomic properties of hazelnut.
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Affiliation(s)
- Ying Li
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Pengchuan Sun
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education & State Key Laboratory of Hydraulics & Mountain River Engineering, College of Life Sciences, Sichuan University, Chengdu, China
| | - Zhiqiang Lu
- CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, 666303, Mengla, Yunnan, China
- Center of Plant Ecology, Core Botanical Gardens, Chinese Academy of Sciences, 666303, Mengla, Yunnan, China
| | - Jinyuan Chen
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Zhenyue Wang
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Xin Du
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Zeyu Zheng
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Ying Wu
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Hongyin Hu
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Jiao Yang
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Jianxiang Ma
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Jianquan Liu
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education & State Key Laboratory of Hydraulics & Mountain River Engineering, College of Life Sciences, Sichuan University, Chengdu, China
| | - Yongzhi Yang
- State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, Lanzhou University, Lanzhou, China.
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177
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Hong I, Rutherford A, Cebrian M. Social mobilization and polarization can create volatility in COVID-19 pandemic control. APPLIED NETWORK SCIENCE 2021; 6:11. [PMID: 33614902 PMCID: PMC7877319 DOI: 10.1007/s41109-021-00356-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 01/28/2021] [Indexed: 05/26/2023]
Abstract
During the COVID-19 pandemic, political polarization has emerged as a significant threat that inhibits coordinated action of central and local institutions reducing the efficacy of non-pharmaceutical interventions (NPIs). Yet, it is not well-understood to what extent polarization can affect grass-roots, voluntary social mobilization targeted at mitigating the pandemic spread. Here, we propose a polarized mobilization model amidst the pandemic for demonstrating the differential responses to COVID-19 as mediated by the USA's political landscape. We use a novel dataset and models from time-critical social mobilization competitions, voting records, and a high-resolution county-wise friendship network. Our simulations show that a higher degree of polarization impedes the overall spread of mobilization and leads to a highly-heterogeneous impact among states. Our hypothetical compliance campaign to mitigate COVID-19 spread predicts grass-roots mitigation strategies' success before the dates of actual lockdowns in identically polarized states with more than three times of success rate than oppositely polarized states. Finally, we analyze the coupling of social mobilization leading to unrest and the growth of COVID-19 infections. These findings highlight social mobilization as both a collective precautionary measure and a potential threat to countermeasures, together with a warning message that the emerging polarization can be a significant hurdle of NPIs relying on coordinated action.
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Affiliation(s)
- Inho Hong
- Center for Humans and Machines, Max Planck Institute for Human Development, Lentzealle 94, 14195 Berlin, Germany
| | - Alex Rutherford
- Center for Humans and Machines, Max Planck Institute for Human Development, Lentzealle 94, 14195 Berlin, Germany
| | - Manuel Cebrian
- Center for Humans and Machines, Max Planck Institute for Human Development, Lentzealle 94, 14195 Berlin, Germany
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178
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China's city-level carbon emissions during 1992-2017 based on the inter-calibration of nighttime light data. Sci Rep 2021; 11:3323. [PMID: 33558535 PMCID: PMC7870850 DOI: 10.1038/s41598-021-81754-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 01/07/2021] [Indexed: 01/30/2023] Open
Abstract
Accurate, long-term, full-coverage carbon dioxide (CO2) data in units of prefecture-level cities are necessary for evaluations of CO2 emission reductions in China, which has become one of the world's largest carbon-emitting countries. This study develops a novel method to match satellite-based Defense Meteorological Satellite Program's Operational Landscan System (DMSP/OLS) and Suomi National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data, and estimates the CO2 emissions of 334 prefecture-level cities in China from 1992 to 2017. Results indicated that the eastern and coastal regions had higher carbon emissions, but their carbon intensity decreased more rapidly than other regions. Compared to previous studies, we provide the most extensive and long-term CO2 dataset to date, and these data will be of great value for further socioeconomic research. Specifically, this dataset provides a foundational data source for China's future CO2 research and emission reduction strategies. Additionally, the methodology can be applied to other regions around the world.
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179
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Beria P, Lunkar V. Presence and mobility of the population during the first wave of Covid-19 outbreak and lockdown in Italy. SUSTAINABLE CITIES AND SOCIETY 2021; 65:102616. [PMID: 33251092 PMCID: PMC7680615 DOI: 10.1016/j.scs.2020.102616] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 11/13/2020] [Accepted: 11/16/2020] [Indexed: 05/18/2023]
Abstract
The non-medical policies implemented by many countries to "flatten the curve" during the COVID-19 outbreak has people stranded in their homes and some, out of their homes unable to return due to the disruptions in the mobility network. The availability of rich datasets (in our case, Facebook) has made it possible to study the mobility dynamics and spatial distribution of people during lockdown in Italy. Our interpretation is an effort to look deeper, describing the movements occurred during lockdown, including the territorial differences. We observe that, initially, tourists left the country and later Italians abroad managed to return, thereby, stabilising the population. With regards to internal mobility, the earliest affected regions see higher number of stationary users in the initial days of the outbreak while this is less significant for the central/southern regions until the decree for the official lockdown on the 9th of March 2020, due 2 days later. Just before lockdown, there was not a significant exodus of people from the North to the rest of the country, instead, relocation of people between cities and their urban belts, but not towards remote areas. This will be elaborated in conclusions shedding light on possible changes in future cities.
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Affiliation(s)
- Paolo Beria
- Dipartimento di Architettura e Studi Urbani, Politecnico di Milano, Italy
| | - Vardhman Lunkar
- Dipartimento di Architettura e Studi Urbani, Politecnico di Milano, Italy
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180
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Thudi M, Palakurthi R, Schnable JC, Chitikineni A, Dreisigacker S, Mace E, Srivastava RK, Satyavathi CT, Odeny D, Tiwari VK, Lam HM, Hong YB, Singh VK, Li G, Xu Y, Chen X, Kaila S, Nguyen H, Sivasankar S, Jackson SA, Close TJ, Shubo W, Varshney RK. Genomic resources in plant breeding for sustainable agriculture. JOURNAL OF PLANT PHYSIOLOGY 2021; 257:153351. [PMID: 33412425 PMCID: PMC7903322 DOI: 10.1016/j.jplph.2020.153351] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/14/2020] [Accepted: 12/14/2020] [Indexed: 05/19/2023]
Abstract
Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.
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Affiliation(s)
- Mahendar Thudi
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India; University of Southern Queensland, Toowoomba, Australia
| | - Ramesh Palakurthi
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Annapurna Chitikineni
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Emma Mace
- Agri-Science Queensland, Department of Agriculture & Fisheries (DAF), Warwick, Australia
| | - Rakesh K Srivastava
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - C Tara Satyavathi
- Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute (IARI), New Delhi, India
| | - Damaris Odeny
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi, Kenya
| | | | - Hon-Ming Lam
- Center for Soybean Research of the State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region
| | - Yan Bin Hong
- Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Vikas K Singh
- South Asia Hub, International Rice Research Institute (IRRI), Hyderabad, India
| | - Guowei Li
- Shandong Academy of Agricultural Sciences, Jinan, China
| | - Yunbi Xu
- International Maize and Wheat Improvement Center (CYMMIT), Mexico DF, Mexico; Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaoping Chen
- Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Sanjay Kaila
- Department of Biotechnology, Ministry of Science and Technology, Government of India, India
| | - Henry Nguyen
- National Centre for Soybean Research, University of Missouri, Columbia, USA
| | - Sobhana Sivasankar
- Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, Vienna, Austria
| | | | | | - Wan Shubo
- Shandong Academy of Agricultural Sciences, Jinan, China
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
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181
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182
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Jimenez-Perez G, Alcaine A, Camara O. Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks. Sci Rep 2021; 11:863. [PMID: 33441632 PMCID: PMC7806759 DOI: 10.1038/s41598-020-79512-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 12/08/2020] [Indexed: 11/16/2022] Open
Abstract
Detection and delineation are key steps for retrieving and structuring information of the electrocardiogram (ECG), being thus crucial for numerous tasks in clinical practice. Digital signal processing (DSP) algorithms are often considered state-of-the-art for this purpose but require laborious rule readaptation for adapting to unseen morphologies. This work explores the adaptation of the the U-Net, a deep learning (DL) network employed for image segmentation, to electrocardiographic data. The model was trained using PhysioNet's QT database, a small dataset of 105 2-lead ambulatory recordings, while being independently tested for many architectural variations, comprising changes in the model's capacity (depth, width) and inference strategy (single- and multi-lead) in a fivefold cross-validation manner. This work features several regularization techniques to alleviate data scarcity, such as semi-supervised pre-training with low-quality data labels, performing ECG-based data augmentation and applying in-built model regularizers. The best performing configuration reached precisions of 90.12%, 99.14% and 98.25% and recalls of 98.73%, 99.94% and 99.88% for the P, QRS and T waves, respectively, on par with DSP-based approaches. Despite being a data-hungry technique trained on a small dataset, a U-Net based approach demonstrates to be a viable alternative for this task.
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Affiliation(s)
- Guillermo Jimenez-Perez
- PhySense research group, BCN-MedTech, Department of Information and Communication Technologies, Barcelona, 08018, Spain.
| | - Alejandro Alcaine
- Facultad de Ciencias de la Salud, Universidad San Jorge, Zaragoza, 05830, Spain
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragón Institute of Engineering Research, Zaragoza, 50018, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, 28029, Spain
| | - Oscar Camara
- PhySense research group, BCN-MedTech, Department of Information and Communication Technologies, Barcelona, 08018, Spain
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183
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Tian X, Chen M. Descriptor selection for predicting interfacial thermal resistance by machine learning methods. Sci Rep 2021; 11:739. [PMID: 33436976 PMCID: PMC7804206 DOI: 10.1038/s41598-020-80795-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 12/28/2020] [Indexed: 01/29/2023] Open
Abstract
Interfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.
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Affiliation(s)
- Xiaojuan Tian
- Department of Chemical Engineering, China University of Petroleum, Beijing, 102249, China.
| | - Mingguang Chen
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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184
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Brousse O, Georganos S, Demuzere M, Dujardin S, Lennert M, Linard C, Snow RW, Thiery W, van Lipzig NPM. Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities? ENVIRONMENTAL RESEARCH LETTERS : ERL [WEB SITE] 2020; 15:124051. [PMID: 35211191 PMCID: PMC7612418 DOI: 10.1088/1748-9326/abc996] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Malaria burden is increasing in sub-Saharan cities because of rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between urban environments and malaria. Additionally, no standardized urban land-use/land-cover has been defined for urban malaria studies. Here, we demonstrate the potential of local climate zones (LCZs) for modeling malaria prevalence rate (Pf PR2-10) and studying malaria prevalence in urban settings across nine sub-Saharan African cities. Using a random forest classification algorithm over a set of 365 malaria surveys we: (i) identify a suitable set of covariates derived from open-source earth observations; and (ii) depict the best buffer size at which to aggregate them for modeling Pf PR2-10. Our results demonstrate that geographical models can learn from LCZ over a set of cities and be transferred over a city of choice that has few or no malaria surveys. In particular, we find that urban areas systematically have lower Pf PR2-10 (5%-30%) than rural areas (15%-40%). The Pf PR2-10 urban-to-rural gradient is dependent on the climatic environment in which the city is located. Further, LCZs show that more open urban environments located close to wetlands have higher Pf PR2-10. Informal settlements-represented by the LCZ 7 (lightweight lowrise)-have higher malaria prevalence than other densely built-up residential areas with a mean prevalence of 11.11%. Overall, we suggest the applicability of LCZs for more exploratory modeling in urban malaria studies.
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Affiliation(s)
- O Brousse
- Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
- UCL Institute for Environmental Design and Engineering, University College London, London, United Kingdom
| | - S Georganos
- Department of Geosciences, Environment and Society, Université Libre de Bruxelles, Brussels, Belgium
| | - M Demuzere
- Department of Geography, Ruhr-University Bochum, Bochum, Germany
- Department of Environment, Ghent University, Ghent, Belgium
| | - S Dujardin
- Department of Geography, Université de Namur, Namur, Belgium
| | - M Lennert
- Department of Geosciences, Environment and Society, Université Libre de Bruxelles, Brussels, Belgium
| | - C Linard
- Department of Geography, Université de Namur, Namur, Belgium
| | - R W Snow
- Population and Health Unit, Kenya Medical Research Institute Wellcome Trust, Nairobi, Kenya
- Department of Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - W Thiery
- Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, Belgium
| | - N P M van Lipzig
- Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
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185
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Yildirim O, Talo M, Ciaccio EJ, Tan RS, Acharya UR. Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105740. [PMID: 32932129 PMCID: PMC7477611 DOI: 10.1016/j.cmpb.2020.105740] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 08/31/2020] [Indexed: 05/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database. METHODS Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers. RESULTS We performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. CONCLUSION Recently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.
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Affiliation(s)
- Ozal Yildirim
- Department of Computer Engineering, Munzur University, Tunceli,62000, Turkey
| | - Muhammed Talo
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Edward J Ciaccio
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Ru San Tan
- National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; School of Management and Enterprise University of Southern Queensland, Springfield, Australia.
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186
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Komura K, Aoki H, Tanaka K, Ikeda T. GAM-3: a zeolite formed from AlPO 4-5 via multistep structural changes. Chem Commun (Camb) 2020; 56:14901-14904. [PMID: 33179643 DOI: 10.1039/d0cc06086k] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The interzeolite conversion of AlPO4-5 gave a new zeolitic material GAM-2 and the calcination caused further structural changes, forming a new zeolite GAM-3 with a 3-dimensional 12-8-6 ring pore system. This is the first synthetic example of a zeolite formed through multistep structural changes in the metastable phase.
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Affiliation(s)
- Kenichi Komura
- Graduated School of Materials Science and Processing Division, Graduated School of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan.
| | - Hisakazu Aoki
- Graduated School of Materials Science and Processing Division, Graduated School of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan.
| | - Kentaro Tanaka
- Graduated School of Materials Science and Processing Division, Graduated School of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan.
| | - Takuji Ikeda
- National Institute of Advanced Industrial Science and Technology (AIST), 4-2-1 Nigatake, Miyagino-ku, Sendai 983-8551, Japan.
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187
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Verstraete N, Jurman G, Bertagnolli G, Ghavasieh A, Pancaldi V, De Domenico M. CovMulNet19, Integrating Proteins, Diseases, Drugs, and Symptoms: A Network Medicine Approach to COVID-19. NETWORK AND SYSTEMS MEDICINE 2020; 3:130-141. [PMID: 33274348 PMCID: PMC7703682 DOI: 10.1089/nsm.2020.0011] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/02/2020] [Indexed: 12/23/2022] Open
Abstract
Introduction: We introduce in this study CovMulNet19, a comprehensive COVID-19 network containing all available known interactions involving SARS-CoV-2 proteins, interacting-human proteins, diseases and symptoms that are related to these human proteins, and compounds that can potentially target them. Materials and Methods: Extensive network analysis methods, based on a bootstrap approach, allow us to prioritize a list of diseases that display a high similarity to COVID-19 and a list of drugs that could potentially be beneficial to treat patients. As a key feature of CovMulNet19, the inclusion of symptoms allows a deeper characterization of the disease pathology, representing a useful proxy for COVID-19-related molecular processes. Results: We recapitulate many of the known symptoms of the disease and we find the most similar diseases to COVID-19 reflect conditions that are risk factors in patients. In particular, the comparison between CovMulNet19 and randomized networks recovers many of the known associated comorbidities that are important risk factors for COVID-19 patients, through identified similarities with intestinal, hepatic, and neurological diseases as well as with respiratory conditions, in line with reported comorbidities. Conclusion: CovMulNet19 can be suitably used for network medicine analysis, as a valuable tool for exploring drug repurposing while accounting for the intervening multidimensional factors, from molecular interactions to symptoms.
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Affiliation(s)
- Nina Verstraete
- Centre de Recherches en Cancérologie de Toulouse (CRCT), UMR1037 Inserm, ERL5294 CNRS, Toulouse, France
- University Paul Sabatier III, Toulouse, France
| | | | | | | | - Vera Pancaldi
- Centre de Recherches en Cancérologie de Toulouse (CRCT), UMR1037 Inserm, ERL5294 CNRS, Toulouse, France
- University Paul Sabatier III, Toulouse, France
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
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188
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Adiga A, Chen J, Marathe M, Mortveit H, Venkatramanan S, Vullikanti A. Data-Driven Modeling for Different Stages of Pandemic Response. J Indian Inst Sci 2020; 100:901-915. [PMID: 33223629 PMCID: PMC7667282 DOI: 10.1007/s41745-020-00206-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 09/15/2020] [Indexed: 12/12/2022]
Abstract
Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who are at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision-making. As different countries and regions go through phases of the pandemic, the questions and data availability also change. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.
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Affiliation(s)
- Aniruddha Adiga
- Biocomplexity Institute and Initiative, Charlottesville, USA
| | - Jiangzhuo Chen
- Biocomplexity Institute and Initiative, Charlottesville, USA
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, Charlottesville, USA.,Department of Computer Science, University of Virginia, Charlottesville, USA
| | - Henning Mortveit
- Biocomplexity Institute and Initiative, Charlottesville, USA.,Department of Systems Engineering and Environment, University of Virginia, Charlottesville, USA
| | | | - Anil Vullikanti
- Biocomplexity Institute and Initiative, Charlottesville, USA.,Department of Computer Science, University of Virginia, Charlottesville, USA
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189
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Wang Q, Lu M, Bai Z, Wang K. Coronavirus pandemic reduced China's CO 2 emissions in short-term, while stimulus packages may lead to emissions growth in medium- and long-term. APPLIED ENERGY 2020; 278:115735. [PMID: 32863540 PMCID: PMC7441887 DOI: 10.1016/j.apenergy.2020.115735] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 07/17/2020] [Accepted: 07/25/2020] [Indexed: 05/03/2023]
Abstract
Coronavirus has confined human activities, which caused significant reductions in coal, oil, and natural gas consumptions in China since January of 2020. We compile industrial, transport, and construction data to estimate the reductions in energy-related CO2 emissions during the first quarter of 2020 in China. Our results show that the fossil fuel related CO2 emissions decreased by 18.7% (182 MtCO2) in the first quarter of 2020 compared with the same period last year, including reductions of 12.2% (92 MtCO2) in industry sectors, 61.9% (62 MtCO2) in transport, and 23.9% (28 MtCO2) in construction. The figure in annual CO2 emission reductions is expected to limit with an estimate of 1.6%. However, to achieve the economic target for the 13th Five-Year-Plan, stimulus packages including investments in "shovel-ready" infrastructure projects issued by China's central and local governments to response the COVID-19 may increase CO2 emissions with a higher speed in the coming years. Thus, sustainable stimulus packages are needed for accelerating China's climate goals.
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Affiliation(s)
- Qingqing Wang
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China
- School of Management and Economics, Beijing Institute of Technology, Beijing, China
| | - Mei Lu
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China
- School of Management and Economics, Beijing Institute of Technology, Beijing, China
| | - Zimeng Bai
- School of Management and Economics, Beijing Institute of Technology, Beijing, China
| | - Ke Wang
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China
- School of Management and Economics, Beijing Institute of Technology, Beijing, China
- Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China
- Beijing Key Lab of Energy Economics and Environmental Management, Beijing, China
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190
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Kjær LJ, Klitgaard K, Soleng A, Edgar KS, Lindstedt HEH, Paulsen KM, Andreassen ÅK, Korslund L, Kjelland V, Slettan A, Stuen S, Kjellander P, Christensson M, Teräväinen M, Baum A, Jensen LM, Bødker R. Spatial patterns of pathogen prevalence in questing Ixodes ricinus nymphs in southern Scandinavia, 2016. Sci Rep 2020; 10:19376. [PMID: 33168841 PMCID: PMC7652892 DOI: 10.1038/s41598-020-76334-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 10/27/2020] [Indexed: 12/17/2022] Open
Abstract
Tick-borne pathogens cause diseases in animals and humans, and tick-borne disease incidence is increasing in many parts of the world. There is a need to assess the distribution of tick-borne pathogens and identify potential risk areas. We collected 29,440 tick nymphs from 50 sites in Scandinavia from August to September, 2016. We tested ticks in a real-time PCR chip, screening for 19 vector-associated pathogens. We analysed spatial patterns, mapped the prevalence of each pathogen and used machine learning algorithms and environmental variables to develop predictive prevalence models. All 50 sites had a pool prevalence of at least 33% for one or more pathogens, the most prevalent being Borrelia afzelii, B. garinii, Rickettsia helvetica, Anaplasma phagocytophilum, and Neoehrlichia mikurensis. There were large differences in pathogen prevalence between sites, but we identified only limited geographical clustering. The prevalence models performed poorly, with only models for R. helvetica and N. mikurensis having moderate predictive power (normalized RMSE from 0.74-0.75, R2 from 0.43-0.48). The poor performance of the majority of our prevalence models suggest that the used environmental and climatic variables alone do not explain pathogen prevalence patterns in Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area.
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Affiliation(s)
- Lene Jung Kjær
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark.
| | - Kirstine Klitgaard
- Department for Diagnostics and Scientific Advice, National Veterinary Institute, Technical University of Denmark, Lyngby, Denmark
| | - Arnulf Soleng
- Department of Pest Control, Norwegian Institute of Public Health, Oslo, Norway
| | | | | | - Katrine M Paulsen
- Department of Virology, Norwegian Institute of Public Health, Oslo, Norway
- Department of Production Animal Clinical Sciences, Norwegian University of Life Sciences, Oslo, Norway
| | | | - Lars Korslund
- Department of Natural Sciences, University of Agder, Kristiansand, Norway
| | - Vivian Kjelland
- Department of Natural Sciences, University of Agder, Kristiansand, Norway
- Research Unit, Sørlandet Hospital Health Enterprise, Kristiansand, Norway
| | - Audun Slettan
- Department of Natural Sciences, University of Agder, Kristiansand, Norway
| | - Snorre Stuen
- Department of Production Animal Clinical Sciences, Section of Small Ruminant Research, Norwegian University of Life Sciences, Sandnes, Norway
| | - Petter Kjellander
- Department of Ecology, Grimsö Wildlife Research Station, Swedish University of Agricultural Sciences, Riddarhyttan, Sweden
| | - Madeleine Christensson
- Department of Ecology, Grimsö Wildlife Research Station, Swedish University of Agricultural Sciences, Riddarhyttan, Sweden
| | - Malin Teräväinen
- Department of Ecology, Grimsö Wildlife Research Station, Swedish University of Agricultural Sciences, Riddarhyttan, Sweden
| | - Andreas Baum
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Laura Mark Jensen
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - René Bødker
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
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191
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Huang Y, Wu Y, Zhang W. Comprehensive identification and isolation policies have effectively suppressed the spread of COVID-19. CHAOS, SOLITONS, AND FRACTALS 2020; 139:110041. [PMID: 32834599 PMCID: PMC7305880 DOI: 10.1016/j.chaos.2020.110041] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 06/16/2020] [Accepted: 06/18/2020] [Indexed: 05/07/2023]
Abstract
The outbreak of COVID-19 has caused severe life and economic damage worldwide. Since the absence of medical resources or targeted therapeutics, systemic containment policies have been prioritized but some critics query what extent can they mitigate this pandemic. We construct a fine-grained transmission dynamics model to forecast the crucial information of public concern, therein using dynamical coefficients to quantify the impact of the implement schedule and intensity of the containment policies on the spread of epidemic. Statistical evidences show the comprehensive identification and quarantine policies eminently contributed to reduce casualties during the phase of a dramatic increase in diagnosed cases in Wuhan and postponing or weakening such policies would undoubtedly exacerbate the epidemic. Hence we suggest that governments should swiftly execute the forceful public health interventions in the initial stage until the pandemic is blocked.
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Affiliation(s)
- Yubo Huang
- The Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yan Wu
- Cancer Hospital Affiliated to Zhengzhou University, Zhengzhou, 450008, China
| | - Weidong Zhang
- The Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
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192
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Abstract
In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.
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Affiliation(s)
- Junaid Shuja
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Islamabad, Pakistan
- Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
- Center of Innovation and Development in Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Eisa Alanazi
- Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
- Center of Innovation and Development in Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Waleed Alasmary
- Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
- Center of Innovation and Development in Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Abdulaziz Alashaikh
- Computer Engineering and Networks Department, University of Jeddah, Jeddah, Saudi Arabia
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193
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Schellenberger S, Shi K, Steigleder T, Malessa A, Michler F, Hameyer L, Neumann N, Lurz F, Weigel R, Ostgathe C, Koelpin A. A dataset of clinically recorded radar vital signs with synchronised reference sensor signals. Sci Data 2020; 7:291. [PMID: 32901032 PMCID: PMC7479598 DOI: 10.1038/s41597-020-00629-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 07/31/2020] [Indexed: 12/01/2022] Open
Abstract
Using Radar it is possible to measure vital signs through clothing or a mattress from the distance. This allows for a very comfortable way of continuous monitoring in hospitals or home environments. The dataset presented in this article consists of 24 h of synchronised data from a radar and a reference device. The implemented continuous wave radar system is based on the Six-Port technology and operates at 24 GHz in the ISM band. The reference device simultaneously measures electrocardiogram, impedance cardiogram and non-invasive continuous blood pressure. 30 healthy subjects were measured by physicians according to a predefined protocol. The radar was focused on the chest while the subjects were lying on a tilt table wired to the reference monitoring device. In this manner five scenarios were conducted, the majority of them aimed to trigger hemodynamics and the autonomic nervous system of the subjects. Using the database, algorithms for respiratory or cardiovascular analysis can be developed and a better understanding of the characteristics of the radar-recorded vital signs can be gained.
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Affiliation(s)
- Sven Schellenberger
- Institute of High-Frequency Technology, Hamburg University of Technology, 21073, Hamburg, Germany.
| | - Kilin Shi
- Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058, Erlangen, Germany
| | - Tobias Steigleder
- Department of Palliative Medicine, Universitätsklinikum Erlangen, Comprehensive Cancer Center CCC Erlangen - EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Anke Malessa
- Department of Palliative Medicine, Universitätsklinikum Erlangen, Comprehensive Cancer Center CCC Erlangen - EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Fabian Michler
- Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058, Erlangen, Germany
| | - Laura Hameyer
- Department of Palliative Medicine, Universitätsklinikum Erlangen, Comprehensive Cancer Center CCC Erlangen - EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Nina Neumann
- Department of Palliative Medicine, Universitätsklinikum Erlangen, Comprehensive Cancer Center CCC Erlangen - EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Fabian Lurz
- Institute of High-Frequency Technology, Hamburg University of Technology, 21073, Hamburg, Germany
| | - Robert Weigel
- Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058, Erlangen, Germany
| | - Christoph Ostgathe
- Department of Palliative Medicine, Universitätsklinikum Erlangen, Comprehensive Cancer Center CCC Erlangen - EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Alexander Koelpin
- Institute of High-Frequency Technology, Hamburg University of Technology, 21073, Hamburg, Germany
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194
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A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic. JOURNAL OF SYSTEMS ARCHITECTURE 2020; 108. [PMCID: PMC7326453 DOI: 10.1016/j.sysarc.2020.101830] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Presented a systematic study of Deep Learning (DL), Deep Transfer Learning (DTL) and Edge Computing (EC) to mitigate COVID-19. Surveyed on existing DL, DTL, EC, and Dataset to mitigate pandemics with potentialities and challenges. Drawn a precedent pipeline model of DTL over EC for a future scope to mitigate any outbreaks. Given brief analyses and challenges wherever relevant in perspective of COVID-19.
Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The spreading and infection factors of this disease are very high. A huge number of people from most of the countries are infected within six months from its first report of appearance and it keeps spreading. The required systems are not ready up to some stages for any pandemic; therefore, mitigation with existing capacity becomes necessary. On the other hand, modern-era largely depends on Artificial Intelligence(AI) including Data Science; and Deep Learning(DL) is one of the current flag-bearer of these techniques. It could use to mitigate COVID-19 like pandemics in terms of stop spread, diagnosis of the disease, drug & vaccine discovery, treatment, patient care, and many more. But this DL requires large datasets as well as powerful computing resources. A shortage of reliable datasets of a running pandemic is a common phenomenon. So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(ED) such as IoT, Webcam, Drone, Intelligent Medical Equipment, Robot, etc. are very useful in a pandemic situation. These types of equipment make the infrastructures sophisticated and automated which helps to cope with an outbreak. But these are equipped with low computing resources, so, applying DL is also a bit challenging; therefore, DTL also would be effective there. This article scholarly studies the potentiality and challenges of these issues. It has described relevant technical backgrounds and reviews of the related recent state-of-the-art. This article also draws a pipeline of DTL over Edge Computing as a future scope to assist the mitigation of any pandemic.
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195
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Luo H, Liu H, Zhang J, Hu B, Zhou C, Xiang M, Yang Y, Zhou M, Jing T, Li Z, Zhou X, Lv G, He W, Zeng B, Xiao S, Li Q, Ye H. Full-length transcript sequencing accelerates the transcriptome research of Gymnocypris namensis, an iconic fish of the Tibetan Plateau. Sci Rep 2020; 10:9668. [PMID: 32541658 PMCID: PMC7296019 DOI: 10.1038/s41598-020-66582-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 05/25/2020] [Indexed: 12/11/2022] Open
Abstract
Gymnocypris namensis, the only commercial fish in Namtso Lake of Tibet in China, is rated as nearly threatened species in the Red List of China's Vertebrates. As one of the highest-altitude schizothorax fish in China, G. namensis has strong adaptability to the plateau harsh environment. Although being an indigenous economic fish with high value in research, the biological characterization, genetic diversity, and plateau adaptability of G. namensis are still unclear. Here, we used Pacific Biosciences single molecular real time long read sequencing technology to generate full-length transcripts of G. namensis. Sequences clustering analysis and error correction with Illumina-produced short reads to obtain 319,044 polished isoforms. After removing redundant reads, 125,396 non-redundant isoforms were obtained. Among all transcripts, 103,286 were annotated to public databases. Natural selection has acted on 42 genes for G. namensis, which were enriched on the functions of mismatch repair and Glutathione metabolism. Total 89,736 open reading frames, 95,947 microsatellites, and 21,360 long non-coding RNAs were identified across all transcripts. This is the first study of transcriptome in G. namensis by using PacBio Iso-seq. The acquisition of full-length transcript isoforms might accelerate the transcriptome research of G. namensis and provide basis for further research.
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Affiliation(s)
- Hui Luo
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
- Key Laboratory of Aquatic Science of Chongqing, 400175, Chongqing, China
| | - Haiping Liu
- Institute of Fisheries Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa, 850000, China
| | - Jie Zhang
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
| | - Bingjie Hu
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
| | - Chaowei Zhou
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
- Key Laboratory of Aquatic Science of Chongqing, 400175, Chongqing, China
| | - Mengbin Xiang
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
| | - Yuejing Yang
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
- Key Laboratory of Aquatic Science of Chongqing, 400175, Chongqing, China
| | - Mingrui Zhou
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
- Key Laboratory of Aquatic Science of Chongqing, 400175, Chongqing, China
| | - Tingsen Jing
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
- Key Laboratory of Aquatic Science of Chongqing, 400175, Chongqing, China
| | - Zhe Li
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
| | - Xinghua Zhou
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
- Key Laboratory of Aquatic Science of Chongqing, 400175, Chongqing, China
| | - Guangjun Lv
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
- Key Laboratory of Aquatic Science of Chongqing, 400175, Chongqing, China
| | - Wenping He
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China
- Key Laboratory of Aquatic Science of Chongqing, 400175, Chongqing, China
| | - Benhe Zeng
- Institute of Fisheries Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa, 850000, China
| | - Shijun Xiao
- Department of Computer Science, Wuhan University of Technology, Wuhan, 430070, China.
| | - Qinglu Li
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China.
| | - Hua Ye
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Southwest University College of Animal Sciences, Chongqing, 402460, China.
- Key Laboratory of Aquatic Science of Chongqing, 400175, Chongqing, China.
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