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1.
Lancet HIV ; 10(6): e412-e420, 2023 06.
Article in English | MEDLINE | ID: covidwho-20242778

ABSTRACT

Cervical cancer is the fourth most common malignancy in women of reproductive age globally. The burden of this disease is highest in low-income and middle-income countries, especially among women living with HIV. In 2018, WHO launched a global strategy to accelerate cervical cancer elimination through rapid scale-up of prophylactic vaccination, cervical screening, and treatment of precancers and cancers. This initiative was key in raising a call for action to address the stark global disparities in cervical cancer burden. However, achieving elimination of cervical cancer among women with HIV requires consideration of biological and social issues affecting this population. This Position Paper shows specific challenges and uncertainties on the way to cervical cancer elimination for women living with HIV and highlights the scarcity of evidence for the effect of interventions in this population. We argue that reaching equity of outcomes for women with HIV will require substantial advances in approaches to HPV vaccination and improved understanding of the long-term effectiveness of HPV vaccines in settings with high HIV burden cervical cancer, just as HIV, is affected by social and structural factors such as poverty, stigma, and gender discrimination, that place the elimination strategy at risk. Global efforts must, therefore, be galvanised to ensure women living with HIV have optimised interventions, given their substantial risk of this preventable malignancy.


Subject(s)
HIV Infections , Papillomavirus Infections , Papillomavirus Vaccines , Uterine Cervical Neoplasms , Female , Humans , Uterine Cervical Neoplasms/epidemiology , Uterine Cervical Neoplasms/prevention & control , Early Detection of Cancer , HIV Infections/complications , HIV Infections/epidemiology , HIV Infections/prevention & control , Papillomavirus Infections/complications , Papillomavirus Infections/prevention & control , Poverty
2.
Applied Economics ; 2023.
Article in English | Scopus | ID: covidwho-2296658

ABSTRACT

Since the outbreak of COVID-19 pandemic, the financial markets of many countries have been impacted severely. In this context, based on the event study method and orthogonal decomposition method, this paper studies the impact of the novel coronavirus epidemic on the spillover effect of global financial risk, and further analyses the financial risk transmission channels of various countries. The results suggest that the novel coronavirus significantly increases the overall risk level of global financial markets, and exacerbates the contagion effects of financial risk through the global risk spillover network. In addition, the analysis of transmission channels reveals the source and direction of the financial risks in each country, manifesting as the unidirectional risk transmissions from developed countries to developing countries and the bidirectional risk contagion paths of countries with similar level of development. Therefore, facing the challenges of public health emergencies such as novel coronavirus epidemic, the major economies should strengthen multilateral cooperation and promote the coordination of macroeconomic policies to jointly defuse global systemic financial risk. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

3.
International Journal of Biomathematics ; 2023.
Article in English | Scopus | ID: covidwho-2287598

ABSTRACT

The spread of infectious diseases often presents the emergent properties, which leads to more difficulties in prevention and treatment. In this paper, the SIR model with both delay and network is investigated to show the emergent properties of the infectious diseases' spread. The stability of the SIR model with a delay and two delay is analyzed to illustrate the effect of delay on the periodic outbreak of the epidemic. Then the stability conditions of Hopf bifurcation are derived by using central manifold to obtain the direction of bifurcation, which is vital for the generation of emergent behavior. Also, numerical simulation shows that the connection probability can affect the types of the spatio-temporal patterns, further induces the emergent properties. Finally, the emergent properties of COVID-19 are explained by the above results. © 2023 World Scientific Publishing Company.

4.
Journal of Computational Science ; 66, 2023.
Article in English | Scopus | ID: covidwho-2246506

ABSTRACT

Traditional classification techniques usually classify data samples according to the physical organization, such as similarity, distance, and distribution, of the data features, which lack a general and explicit mechanism to represent data classes with semantic data patterns. Therefore, the incorporation of data pattern formation in classification is still a challenge problem. Meanwhile, data classification techniques can only work well when data features present high level of similarity in the feature space within each class. Such a hypothesis is not always satisfied, since, in real-world applications, we frequently encounter the following situation: On one hand, the data samples of some classes (usually representing the normal cases) present well defined patterns;on the other hand, the data features of other classes (usually representing abnormal classes) present large variance, i.e., low similarity within each class. Such a situation makes data classification a difficult task. In this paper, we present a novel solution to deal with the above mentioned problems based on the mesostructure of a complex network, built from the original data set. Specifically, we construct a core–periphery network from the training data set in such way that the normal class is represented by the core sub-network and the abnormal class is characterized by the peripheral sub-network. The testing data sample is classified to the core class if it gets a high coreness value;otherwise, it is classified to the periphery class. The proposed method is tested on an artificial data set and then applied to classify x-ray images for COVID-19 diagnosis, which presents high classification precision. In this way, we introduce a novel method to describe data pattern of the data "without pattern” through a network approach, contributing to the general solution of classification. © 2022 Elsevier B.V.

5.
Acupuncture and Herbal Medicine ; 2(3):152-61, 2022.
Article in English | PubMed Central | ID: covidwho-2161217

ABSTRACT

To systematically review the clinical practice guidelines (CPGs) for the treatment of patients with coronavirus disease 2019 (COVID-19) using Chinese herbal medicine (CHM), assess the methodological quality as well as clinical credibility and implementability of specific recommendations, and summarize key recommendations.Methods:: As of April 2022, we conducted a comprehensive search on major electronic databases, guideline websites, academic society websites, and government websites to assess the methodological quality and clinical applicability of the included CPGs using the Appraisal of Guidelines for Research and Evaluation (AGREE) II tool and Evaluation-Recommendations EXcellence (AGREE-REX) instructions, respectively. Results:: The search yielded 61 CPGs, which were mostly published in 2020;moreover, 98.4% of the CPGs were published in China. Only five CPGs achieved a high-quality AGREE II rating;further, six CPGs could be directly recommended, with most of the CPGs still showing much room for improvement. CPGs had a low overall score in the AGREE-REX evaluation, with the domains of clinical applicability, values and preferences, and implementability being standardized in 21.80% ± 12.56%, 16.00% ± 11.81%, and 31.33% ± 14.55% of the CPGs, respectively. Five high-quality CPGs mentioned 56 Chinese herbal formulas. Half of the recommendations had moderate or strong evidence level in the GRADE evaluation. The most frequently recommended herbal medicines were Lianhua Qingwen granule/capsule and Jinhua Qinggan granule;however, the strength of recommendation for each prescription varied across CPGs and populations. Conclusions:: The overall quality of current CPGs for COVID-19 for CHM still needs to be improved;moreover, the strength of the evidence remains to be standardized across CPGs. Graphical :: http://links.lww.com/AHM/A34.

6.
International Journal of Manpower ; 2022.
Article in English | Web of Science | ID: covidwho-1997106

ABSTRACT

Purpose Due to the fact that most employees have been forced to work remotely during the lockdown resulting from the COVID-19 pandemic, there is great concern about how to alleviate increased stress among employees through human resource (HR) practices. Drawing upon the job demands-control (JDC) model and the job demands-resources (JDR) model, this study empirically investigated the direct effect of HR practices on employee stress in enforced remote work and the mediating role of sources of stress (SoS) and sense of control (SoC). Design/methodology/approach Data were collected through an online survey platform called Wenjuanxing from March 15 to 22, 2020 in Hubei, China and from April 22 to 29, 2022 in Shanghai, China. Respondents scanned the QR code on WeChat to enter the platform. A total of 511 valid questionnaires were received with a response rate of 75.4%. After controlling demographic variables, the authors used the mediation modeling and PROCESS tool to test the proposed hypotheses. Findings HR practices negatively affect stress in enforced remote work among employees. Both SoS and SoC partially mediate the relationship between HR practices and stress. HR practices can alleviate stress via decreasing SoS and enhancing SoC, respectively. Moreover, employee care and training are found to be two key factors of HR practices to help employees alleviate stress in enforced remote work. Originality/value Lockdown as an extreme external condition has brought great challenges in employee work arrangement as well as HR practices. Although the relationship between HR practices and job stress was studied previously, there is a lack of research on the effects of HR practices on stress in enforced remote work due to lockdown. It advances knowledge on HR practices' stress-reducing effect in the context of remote work and provides suggestions for HR practitioners on ways of alleviating employee stress in remote work.

7.
Topics in Antiviral Medicine ; 30(1 SUPPL):18-19, 2022.
Article in English | EMBASE | ID: covidwho-1880917

ABSTRACT

Background: Real-world evidence on effectiveness of booster or additional doses of COVID-19 vaccine is limited. Methods: Using patient-level data from 50 sites in the U.S. National COVID Cohort Collaborative (N3C), we estimated COVID-19 booster vaccine effectiveness compared to full vaccination alone (completed 2 doses mRNA or 1 dose Janssen vaccine). At each month following full vaccination, we created comparable cohorts of patients with boosters propensity-score matched to those without boosters by age, sex, race/ethnicity, comorbidities, geographic region, prior COVID-19 infection, and calendar month of full vaccination. Booster efficacy was evaluated among patients with and without immunosuppressed/compromised conditions (ISC;HIV infection, solid organ or bone marrow transplant, autoimmune diseases, and cancer). We used Cox regression models to estimate hazards of breakthrough infection (COVID-19 diagnosis after last dose of vaccine) and logistic regression models to compare the risk of death ≤45 days after a breakthrough infection in the boosted vs. matched non-boosted groups. Results: By 11/18/2021, 656390 patients had received full vaccination, and 125409 fully vaccinated had received an additional booster (median time from last vaccine to booster dose: 7.4 months, IQR:6.6, 8.2). At completion of full vaccination, median age was 50 (IQR 33-64) years, 43% male, 50% white, 11% Black, 18% Latinx, 4.8% Asian American/Pacific Islander, and 20% had ISC. People receiving a booster were more likely to be older, male, white, and have ISC. Booster vaccine was significantly associated with a reduced hazard of breakthrough infection (Table). Booster efficacy ranged from 46% (booster receipt 1-4 months after full vaccination) to 83% (receipt 7 months after full vaccination) in people without ISC. Vaccine efficacy was lower, ranging from 43%-65%, in ISC patients (Table). Compared to fully vaccinated patients without booster receipt, patients with booster had an 83% (OR: 0.17, 95% CI: 0.11, 0.28) reduced risk of COVID-19 related death, independent of demographics, geographic region, comorbidities, ISC, prior COVID-19 infection, and time of full vaccination. Conclusion: A booster dose of COVID-19 vaccine has high effectiveness in reducing breakthrough infection risk among all fully vaccinated individuals, though only with moderate effectiveness among ISC patients. Nonetheless, booster vaccination significantly reduced risk for COVID-19 related death regardless of ISC status.

8.
EPL ; 137(4), 2022.
Article in English | Scopus | ID: covidwho-1846890

ABSTRACT

It is well known that the outbreak of infectious diseases is affected by the diffusion of the infected. However, the diffusion network is seldom considered in the network-organized SIR model. In this work, we investigate the effect of the maximum eigenvalue on Turing instability and show the role of network parameters (the network connection rate, the network's infection, etc.) on the outbreak of infectious diseases. Meanwhile, stability of network-organized SIR is given by using the maximum eigenvalue of the network matrix which is proportional to the network connection rate and the networks infection rate. The bridge between the two rates and Turing instability was also revealed which can explain the spread mechanism of infectious diseases. In the end, some measures to mitigate the spread of infectious diseases are proposed and the feasible strategies for prevention and control can be provided in our paper, the data from COVID-19 validated the above results. © Copyright © 2022 EPLA.

9.
Aims Mathematics ; 7(5):9288-9310, 2022.
Article in English | Web of Science | ID: covidwho-1760885

ABSTRACT

An immunogenic and safe vaccine against COVID-19 for use in the healthy population will become available in the near future. In this paper, we aim to determine the optimal vaccine administration strategy in refugee camps considering maximum daily administration and limited total vaccine supply. For this purpose, extended SEAIRD compartmental models are established to describe the epidemic dynamics with both single-dose and double-dose vaccine administration. Taking the vaccination rates in different susceptible compartments as control variables, the optimal vaccine administration problems are then solved under the framework of nonlinear constrained optimal control problems. To the best of our knowledge, this is the first paper that addresses an optimal vaccine administration strategy considering practical constraints on limited medical care resources. Numerical simulations show that both the single-dose and double-dose strategies can successfully control COVID-19. By comparison, the double-dose vaccination strategy can achieve a better reduction in infection and death, while the single-dose vaccination strategy can postpone the infection peak more efficiently. Further studies of the influence of parameters indicate that increasing the number of medical care personnel and total vaccine supply can greatly contribute to the fight against COVID-19. The results of this study are instructive for potential forthcoming vaccine administration. Moreover, the work in this paper provides a general framework for developing epidemic control strategies in the presence of limited medical resources.

10.
2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; : 3181-3184, 2021.
Article in English | Scopus | ID: covidwho-1722897

ABSTRACT

The COVID-19 pandemic has had a severe impact on humans' lives and and healthcare systems worldwide. How to early, fastly and accurately diagnose infected patients via multimodal learning is now a research focus. The central challenges in this task mainly lie on multi-modal data representation and multi-modal feature fusion. To solve such challenges, we propose a medical knowledge enriched multi-modal sequence to sequence learning model, termed MedSeq2Seq. The key components include two attention mechanisms, viz. intra-modal (Ia) and inter-model (Ie) attentions, and a medical knowledge augmentation mechanism. The former two mechanisms are to learn multi-modal refined representation, while the latter aims to incorporate external medical knowledge into the proposed model. The experimental results show the effectiveness of the proposed MedSeq2Seq framework over state-of-the-art baselines with a significant improvement of 1%-2%. © 2021 IEEE.

11.
10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 ; 1015:39-49, 2022.
Article in English | Scopus | ID: covidwho-1626567

ABSTRACT

In real world data classification tasks, we always face the situations where the data samples of the normal cases present a well defined pattern and the features of abnormal data samples vary from one to another, i.e., do not show a regular pattern. Up to now, the general data classification hypothesis requires the data features within each class to present a certain level of similarity. Therefore, such real situations violate the classic classification condition and make it a hard task. In this paper, we present a novel solution for this kind of problems through a network approach. Specifically, we construct a core-periphery network from the training data set in such way that core node set is formed by the normal data samples and peripheral node set contains the abnormal samples of the training data set. The classification is made by checking the coreness of the testing data samples. The proposed method is applied to classify radiographic image for COVID-19 diagnosis. Computer simulations show promising results of the method. The main contribution is to introduce a general scheme to characterize pattern formation of the data “without pattern”. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

12.
10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 ; 1015:16-26, 2022.
Article in English | Scopus | ID: covidwho-1626517

ABSTRACT

An important task in combating COVID-19 involves the quick and correct diagnosis of patients, which is not only critical to the patient’s prognosis, but can also help to optimize the configuration of hospital resources. This work aims to classify chest radiographic images to help the diagnosis and prognosis of patients with COVID-19. In comparison to images of healthy lungs, chest images infected by COVID-19 present geometrical deformations, like the formation of filaments. Therefore, fractal dimension is applied here to characterize the levels of complexity of COVID-19 images. Moreover, real data often contains complex patterns beyond physical features. Complex networks are suitable tools for characterizing data patterns due to their ability to capture the spatial, topological and functional relationship between the data. Therefore, a complex network-based high-level data classification technique, capable of capturing data patterns, is modified and applied to chest radiographic image classification. Experimental results show that the proposed method can obtain high classification precision on X-ray images. Still in this work, a comparative study between the proposed method and the state-of-the-art classification techniques is also carried out. The results show that the performance of the proposed method is competitive. We hope that the present work generates relevant contributions to combat COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

13.
Journal of Image and Graphics ; 26(9):2171-2180, 2021.
Article in Chinese | Scopus | ID: covidwho-1438901

ABSTRACT

Objective: The outbreak of corona virus disease 2019 (COVID-19) has become a serious public health event of concern worldwide. The key to controlling the spread of this disease is early detection. Computed tomography (CT) is highly sensitive to the early diagnosis of patients with COVID-19, and the changes in clinical symptoms are time-consistent with the changes in lung CT lesions, which is a simpler, faster indicator for judging changes in the condition. Faint ground-glass opacity is common in the early stage of COVID-19 lesions, and the ground-glass opacity gradually increases as the lesion progresses. Manual detection methods are time consuming, and manual detection inevitably has subjective diagnostic errors. In recent years, deep learning has made great progress in computer vision and achieved outstanding performance in the detection of lung CT scans. In the target detection task, the two-stage target detection method easily achieves a higher precision. The most representative model is faster region convolutional neural network (Faster R-CNN). However, with the increasing diversification and complexity of target detection tasks, the shortcomings of the Faster R-CNN model have also been exposed. In the detection of the ground-glass opacity target, the target size range is large, and Faster R-CNN only uses the highest layer feature map to obtain the region proposal, which has the problem of low recognition rate for small targets. When the region proposal network of Faster R-CNN model supervises the foreground/background classification, most of the overlap calculations between the anchor boxes and the background area are redundant calculations. in the task of detecting ground-glass opacity targets in CT scans of the lung and given the problems of the Faster R-CNN model, an improved method for the feature extraction network and region proposal network of the Faster R-CNN model is proposed. Method: First, the feature pyramid network replaces the feature extraction network of Faster R-CNN to generate a feature pyramid. Then, the region proposal network based on location mapping generates anchor boxes and calculates the distance from the center of each anchor boxes to the center of the real object, which is represented by the parameter "centrality". The anchor box judged as the foreground by the region proposal network is further modified as a region proposal, and the foreground/background classification confidence predicted by the region proposal network and centrality are combined as the sorting basis for the region proposal. The interest regions are filtered out from region proposals through non-maximal suppression. Finally, the characteristic regions corresponding to regions of interest are sent to the classification regression network to obtain the detection results. Content of main experiments, the experiment uses recall, mean average precision (mAP), and frames per second (FPS) as evaluation indicators to compare the performance of the standard Faster R-CNN, Faster R-CNN + FPS, and the proposed model, and the effects of different backbone networks on the model in this paper. Result: On the dataset of COVID-19, the experimental results show that compared with the Faster R-CNN model, the improved model increases recall by 7%, mAP by 3.9%, and FPS from 5 to 9. Conclusion: The improved model can effectively detect the ground-glass opacity target of the patient's lung CT scans and is suitable for small targets. The improved region proposal network reduces network output parameters, saves calculation time, and increases model running speed. Meanings using the feature pyramid network to replace the feature extraction network of Faster R-CNN can be a general method to solve the problem of a large size range of target objects. The method of using the location mapping-based region proposal network to replace the traditional multianchor box mapping-based region proposal network can also provide a reference for accelerating the running speed of the model. © 2021, Editorial Office of Journal of Image and Graphics. All ri ht reserved.

14.
Chinese Economy ; 2021.
Article in English | Scopus | ID: covidwho-1132278

ABSTRACT

As the rest of the world struggled to cope with the COVID-19 pandemic, China was the only major world economy to report growth in 2020. However, challenges still lie ahead. This special issue collects six articles that discuss the challenges and opportunities facing the Chinese Economy, addressing issues in food safety and regulation, the impact of COVID-19 on consumer and labor markets, and new opportunities in E-commerce and trade. These articles provide insights that can be useful for businesses and policy makers to navigate the impact of COVID-19, and draw implications that can last well into the new decade. © 2021 Taylor & Francis Group, LLC.

16.
Non-conventional | WHO COVID | ID: covidwho-95755

ABSTRACT

2019 novel coronavirus disease has resulted in thousands of critically ill patients in China, which is a serious threat to people's life and health. Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) was reported to share the same receptor, angiotensin-converting enzyme 2 (ACE2), with SARS-CoV. Here, based on the public single-cell RNA-sequencing database, we analyzed the mRNA expression profile of putative receptor ACE2 and AXL receptor tyrosine kinase (AXL) in the early maternal-fetal interface. The result indicates that the ACE2 has very low expression in the different cell types of early maternal-fetal interface, except slightly high in decidual perivascular cells cluster 1 (PV1). Interestingly, we found that the Zika virus (ZIKV) receptor AXL expression is concentrated in perivascular cells and stromal cells, indicating that there are relatively more AXL-expressing cells in the early maternal-fetal interface. This study provides a possible infection route and mechanism for the SARS-CoV-2- or ZIKV-infected mother-to-fetus transmission disease, which could be informative for future therapeutic strategy development. Zheng Qing-Liang 1 Clinical and Translational Research Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai 201204 Duan Tao 2 Clinical and Translational Research Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai 201204 Jin Li-Ping 3 Clinical and Translational Research Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai 201204 Wang W, Tang J, Wei F. Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China. J Med Virol 2020. [Ahead of print]. doi: 10.1002/jmv.25689. Chen H, Guo J, Wang C, Luo F, Yu X, Zhang W, et al. Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: A retrospective review of medical records. Lancet 2020. [Ahead of print]. doi: 10.1016/S0140-6736(20) 30360-3. Xu X, Chen P, Wang J, Feng J, Zhou H, Li X, et al. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Sci China Life Sci 2020. [Ahead of print]. doi: 10.1007/s11427-020-1637-5. Li W, Sui J, Huang IC, Kuhn JH, Radoshitzky SR, Marasco WA, et al. The S proteins of human coronavirus NL63 and severe acute respiratory syndrome coronavirus bind overlapping regions of ACE2. Virology 2007;367:367-74. doi: 10.1016/j.virol.2007.04.035. Ferreira LM, Meissner TB, Tilburgs T, Strominger JL. HLA-G: At the interface of maternal-fetal tolerance. Trends Immunol 2017;38:272-86. doi: 10.1016/j.it.2017.01.009. Picelli S, Faridani OR, Björklund AK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 2014;9:171-81. doi: 10.1038/nprot.2014.006. Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 2018;563:347-53. doi: 10.1038/s41586-018-0698-6. Zhao Y, Zhao Z, Wang Y, Zhou Y, Ma Y, Zuo W. Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov. BioRxiv 2020. [Ahead of print]. doi: 10.1101/2020.01.26.919985. Lazear HM, Diamond MS. Zika virus: New clinical syndromes and its emergence in the western hemisphere. J Virol 2016;90:4864-75. doi: 10.1128/JVI.00252-16. Petersen LR, Jamieson DJ, Powers AM, Honein MA. Zika virus. N Engl J Med 2016;374:1552-63. doi: 10.1056/NEJMra1602113. Rasmussen SA, Jamieson DJ, Honein MA, Petersen LR. Zika virus and birth defects - Reviewing the evidence for causality. N Engl J Med 2016;374:1981-7. doi: 10.1056/NEJMsr1604338. Brasil P, Pereira JP Jr., Moreira ME, Ribeiro Nogueira RM, Damasceno L, Wakimoto M, et al. Zika virus infection in pregnant women in Rio de Janeiro. N Engl J Med 2016;375:2321-34. doi: 10.1056/NEJMoa1602412. Tabata T, Petitt M, Puerta-Guardo H, Michlmayr D, Wang C, Fang-Hoover J, et al. Zika virus targets different primary human placental cells, suggesting two routes for vertical transmission. Cell Host Microbe 2016;20:155-66. doi: 10.1016/j.chom.2016.07.002. Quicke KM, Bowen JR, Johnson EL, McDonald CE, Ma H, O'Neal JT, et al. Zika virus Infects Human Placental Macrophages. Cell Host Microbe 2016;20:83-90. doi: 10.1016/j.chom.2016.05.015.

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