bluuebunny/crossref_metadata_2025 huggingface dataset:
Genuage: visualizing and analyzing multidimensional point cloud data in virtual reality
Authors: Blanc Thomas , El Beheiry Mohamed , Masson Jean-Baptiste , Hajj Bassam
Published: March 2020

The quantity of experimentally recorded point cloud data, such generated in single-molecule experiments, is increasing continuously in both size and dimension. Gaining an intuitive understanding of the data and facilitating multi-dimensional data analysis remains a challenge. It is especially challenging when static distribution properties are not predictive of dynamical properties. Here, we report a new open-source software platform, Genuage, that enables the easy perception, interaction and analysis of complex multidimensional point cloud datasets by leveraging virtual reality. We illustrate the benefit of the Genuage with examples of three-dimensional static and dynamic localization microscopy datasets, as well as some synthetic datasets. Genuage has a large breadth of usage modes, due to its compatibility with arbitrary multidimensional data types extending beyond the single-molecule research community.
Analysis and Prediction of COVID-19 Patients’ False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective Study
Authors: Xu Hui , Yan Li , Qiu Chun (Martin) , Jiao Bo , Chen Yanyan , Tan Xi , Chen Zhuo , Ai Ling , Xiao Yaru , Luo Ailin , Li Shusheng
Published: March 2020

Background

False negative results of SARS-CoV-2 nucleic acid detection pose threats to COVID-19 patients and medical workers alike.

Objective

To develop multivariate models to determine clinical characteristics that contribute to false negative results of SARS-CoV-2 nucleic acid detection, and use them to predict false negative results as well as time windows for testing positive.

Design

Retrospective Cohort Study (Ethics number of Tongji Hospital: No. IRBID: TJ-20200320)

Setting

A database of outpatients in Tongji Hospital (University Hospital) from 15 January 2020 to 19 February 2020.

Patients

1,324 outpatients with COVID-19

Measurements

Clinical information on CT imaging reports, blood routine tests, and clinic symptoms were collected. A multivariate logistic regression was used to explain and predict false negative testing results of SARS-CoV-2 detection. A multivariate accelerated failure model was used to analyze and predict delayed time windows for testing positive.

Results

Of the 1,324 outpatients who diagnosed of COVID-19, 633 patients tested positive in their first SARS-CoV-2 nucleic acid test (47.8%), with a mean age of 51 years (SD=14.9); the rest, which had a mean age of 47 years (SD=15.4), tested negative in the first test. “Ground glass opacity” in a CT imaging report was associated with a lower chance of false negatives (aOR, 0.56), and reduced the length of time window for testing positive by 26%. “Consolidation” was associated with a higher chance of false negatives (aOR, 1.57), and extended the length of time window for testing positive by 44%. In blood routine tests, basophils (aOR, 1.28) and eosinophils (aOR, 1.29) were associated with a higher chance of false negatives, and were found to extend the time window for testing positive by 23% and 41%, respectively. Age and gender also affected the significantly.

Limitation

Data were generated in a large single-center study.

Conclusion

Testing outcome and positive window of SARS-CoV-2 detection for COVID-19 patients were associated with CT imaging results, blood routine tests, and clinical symptoms. Taking into account relevant information in CT imaging reports, blood routine tests, and clinical symptoms helped reduce a false negative testing outcome. The predictive AFT model, what we believe to be one of the first statistical models for predicting time window of SARS-CoV-2 detection, could help clinicians improve the accuracy and efficiency of the diagnosis, and hence, optimizes the timing of nucleic acid detection and alleviates the shortage of nucleic acid detection kits around the world.

Primary Funding Source

None.
Risks of use and non-use of antibiotics in primary care. Qualitative study of prescribers’ views
Authors: Boiko Olga , Burgess Caroline , Fox Robin , Ashworth Mark , Gulliford Martin C
Published: March 2020

Purpose

The emergence of antimicrobial resistance has led to increasing efforts to reduce unnecessary use of antibiotics in primary care, but potential hazards from bacterial infection continue to cause concern. This study investigated how primary care prescribers evaluate the risks of reduced antibiotic prescribing.

Methods

Qualitative study using semi-structured interviews conducted with primary care prescribers from 10 general practices in an urban area and a shire town in England. A thematic analysis was conducted.

Results

Thirty participants were recruited, including 23 general practitioners, 5 nurses and 2 pharmacists. Three main themes were identified: risk assessment; balancing treatment risks; and negotiating decisions and risks. Respondents indicated that their decisions were grounded in clinical risk assessment, but this was informed by different approaches to antibiotic use, with most leaning towards reduced prescribing. Prescribers’ perceptions of risk included the consequences of both inappropriate prescribing and inappropriate withholding of antibiotics. Sepsis was viewed as the most concerning potential outcome of non-prescribing, leading to possible patient harm and potential litigation. Risks of antibiotic prescribing included antibiotic resistant and C. difficile infections, as well as side effects, such as rashes, that might lead to possible mislabelling as antibiotic allergy. Prescribers elicited patient preferences for use or avoidance of antibiotics to inform management strategies, which included educational advice, advice on self-management including warning signs, use of delayed prescriptions, and safety netting.

Conclusions

Attitudes towards antibiotic prescribing are evolving, with reduced antibiotic prescribing now being approached more systematically. The safety trade-offs associated with either use or non-use of antibiotics present difficulties especially when prescribing decisions are inconsistent with patients’ expectations.

Strengths and limitations of this study

The study provides an investigation of primary care prescribers’ perceptions, emphasising safety perspectives in the context of antimicrobial stewardship.

The main themes identified may inform the basis for future improvement and antimicrobial stewardship programs.

The study is based on interviews with prescribers and may be susceptible to the limitations associated with qualitative interview studies

The diverse sample of participants provide a good spread of opinions that are of a high validity and rigorously analysed.

The study may lack generalisability beyond high-income countries.
Clear plastic drapes may be effective at limiting aerosolization and droplet spray during extubation: implications for COVID-19
Authors: Matava C.T. , Yu J. , Denning S.
Published: March 2020

Protection of frontline HCPs is paramount. However, PPE is a limited resource and often requires providers to be adaptive and resourceful in a crisis. The inexpensive and simple method of using clear drapes during extubation (and possibly intubation) of COVID-19 patients may be considered by frontline HCPs and infection control specialists as an additional precaution. Modifications of the clear plastics can be adapted for surgical procedures that may be AGMPs.
Trend Analysis and Forecasting of COVID-19 outbreak in India
Authors: Gupta Rajan , Pal Saibal K
Published: March 2020

Abstract

COVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holt’s linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.
Extreme Climate Related Disasters: two-time points evaluation of the impact in children and youth mental health
Authors: de Sousa Magalhães Sabrina , Malloy-Diniz Leandro Fernandes , Valadão Rosa Daniela , Alvim-Soares Antônio , de Miranda Débora Marques , Aurélio Romano-Silva Marco
Published: March 2020

Worldwide, floods and drought are the most frequent extreme climate-related disasters with a potential that might affect children and adolescent mental health. This study aimed to describe mental health impact on youth exposed to flood or drought (time 1), to provide a prospective follow up of symptoms (time 2), about 15 months apart, and to compare the child response with control groups based on ranges of socioeconomic status. Posttraumatic stress symptoms (PTSS) and general behavior problems were evaluated. Sociodemographic data from 275 children and adolescents (6 to 18 years old) were described and analyzed and they were gathered in four groups Control group with higher socioeconomic status, Control group with lower socioeconomic status, Flood group, and Drought group. At time point 1, children from all groups did not substantially differ in general behavioral problems, but PTSS scores significantly differentiate the groups. At time point 2, the Flood group presented a pattern of recovery about PTSS. For the Drought group, a persistence and stability pattern of PTSS was verified. Post-disaster longitudinal studies are essential to elucidate how psychological distress progress over time and to understand the relationship between mental health and exposure to trauma.
Why estimating population-based case fatality rates during epidemics may be misleading
Authors: Böttcher Lucas , Xia Mingtao , Chou Tom
Published: March 2020

Different ways of calculating mortality ratios during epidemics can yield widely different results, particularly during the COVID-19 pandemic. We formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality ratios. The key parameters that affect the dynamics of the different mortality estimates are the incubation period and the length of time individuals were infected before confirmation of infection. We stress that none of these ratios are accurately represented by the often misinterpreted case fatality ratio (CFR), the number of deaths to date divided by the total number of infected cases to date. Using available data on the recent SARS-CoV-2 outbreaks and simple assumptions, we estimate and compare the different dynamic mortality ratios and highlight their differences. Informed by our modeling, we propose a more systematic method to determine mortality ratios during epidemic outbreaks and discuss sensitivity to confounding effects and errors in the data.
Persistent SARS-CoV-2 presence is companied with defects in adaptive immune system in non-severe COVID-19 patients
Authors: Liu Bing , Han Junyan , Cheng Xiaohuan , Yu Long , Zhang Li , Wang Wei , Ni Lan , Wei Chaojie , Huang Yafei , Cheng Zhenshun
Published: March 2020

Background

COVID-19 has been widely spreading. We aim to examine adaptive immune cells in non-severe patients with persistent SARS-CoV-2 shedding.

Methods

37 non-severe patients with persistent SARS-CoV-2 presence transferred to Zhongnan hospital of Wuhan University were retrospectively recruited to PP (persistently positive) group, which was further allocated to PPP group (n=19) and PPN group (n=18), according to their testing results after 7 days (N=negative). Epidemiological, demographic, clinical and laboratory data were collected and analyzed. Data from age- and sex-matched non-severe patients at disease onset (PA [positive on admission] patients, n=37), and lymphocyte subpopulation measurements from matched 54 healthy subjects were extracted for comparison.

Results

Compared with PA patients, PP patients had much improved laboratory findings, including WBCs, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, albumin, AST, CRP, SAA, and IL-6. The absolute numbers of CD3+ T cells, CD4+ T cells, and NK cells were significantly higher in PP group than that in PA group, and were comparable to that in healthy controls. PPP subgroup had markedly reduced B cells and T cells compared to PPN group and healthy subjects. Finally, paired results of these lymphocyte subpopulations from 10 PPN patients demonstrated that the number of T cells and B cells significantly increased when the SARS-CoV-2 tests turned negative.

Conclusion

Persistent SARS-CoV-2 presence in non-severe COVID-19 patients is associated with reduced numbers of adaptive immune cells. Monitoring lymphocyte subpopulations could be clinically meaningful in identifying fully recovered COVID-19 patients.

Defects in adaptive immune system, including reduced T cells and B cells, were frequently observed in non-severe COVID-19 patients with persistent SARS-CoV-2 shedding. Assessment of immune system could be clinically relevant for discharge management.
Generating high quality assemblies for genomic analysis of transposable elements
Authors: Wierzbicki Filip , Schwarz Florian , Cannalonga Odontsetseg , Kofler Robert
Published: March 2020

The advent of long-read sequencing holds great promise for research on transposable elements (TEs). Long reads may finally allow us to obtain reliable assemblies of repetitive regions, and thus shed light on many open questions in TE biology, such as the evolution of piRNA clusters, i.e., the master loci controlling TE activity. Currently, many different assembly strategies exist and it is not clear how to obtain the most suitable assemblies for TE research. In fact, it is not even clear how to best identify suitable assemblies as classic quality metrics such as BUSCO and NG50 are ignorant of TEs. To address these problems, we introduce four novel quality metrics that assess i) how well piRNA clusters are assembled (CUSCO) and ii) to which extent an assembly captures the TE landscape of an organism (TE abundance, SNPs and internal deletions). Using these novel metrics, we evaluate the effect of assemblers, polishing, read length, coverage, residual polymorphisms, and finally, identify suitable assembly strategies. Using an optimized approach, we provide high-quality assemblies for the twoDrosophila melanogasterstrains Canton-S and Pi2. Around 80% of the piRNA clusters were contiguously assembled in these two strains. Such high-quality assemblies will provide novel insights into the biology of TEs. It is, for example, an open question of whether piRNA clusters contain abundant presence/absence polymorphism of TE insertions, as expected when piRNA clusters are responsible for stopping TE invasions. A comparison of the sequences of our assembled piRNA clusters reveals that such polymorphisms are indeed abundantly found in clusters.
Multimodal brain imaging study of 36,678 participants reveals adverse effects of moderate drinking
Authors: Daviet Remi , Aydogan Gökhan , Jagannathan Kanchana , Spilka Nathaniel , Koellinger Philipp D. , Kranzler Henry R. , Nave Gideon , Wetherill Reagan R.
Published: March 2020

Heavy alcohol consumption can have significant deleterious neural consequences, including brain atrophy, neuronal loss, poorer white matter fiber integrity, and cognitive decline. However, the effects of light-to-moderate alcohol consumption on brain structure remain unclear. Here, we examine the associations between alcohol intake and brain structure using multimodal imaging data from 36,678 generally healthy middle-aged and older adults from the UK Biobank, controlling for numerous potential confounds. We find negative associations between alcohol intake and global gray matter volume (GMV) and white matter volume (WMV), which become stronger as intake increases. An examination of the associations between alcohol intake and 139 regional GMV imaging-derived phenotypes (IDPs) and 375 WM microstructure IDPs yielded 304 (59.1%) significant findings, including 125 GMV IDPs that are spread across the brain and 179 WM microstructure IDPs across multiple tract regions. In general, findings comport with the existing literature. However, a daily alcohol intake of as little as one to two units – 250 to 500 ml of a 4% beer or 76 to 146 ml of a 13% wine – is already associated with GMV deficits and altered WMV microstructure, placing moderate drinkers at risk.

One Sentence Summary

Moderate alcohol intake, consuming one or more daily alcohol units, has adverse effects on brain health.
Failed detection of the full-length genome of SARS-CoV-2 by ultra-deep sequencing from the recovered and discharged patients retested viral PCR positive
Authors: Hu Fengyu , Chen Fengjuan , Wang Yaping , Xu Teng , Tang Xiaoping , Li Feng
Published: March 2020

Over 10 percent of recovered and discharged patients retested positive for SARS-CoV-2, raising a public health concern whether they could be potential origins of infection. In this study, we found that detectable viral genome in discharged patients might only mean the presence of viral fragments, and could hardly form an infection origin for its extremely low concentration.
Evaluating the effectiveness of social distancing interventions against COVID-19
Authors: Matrajt Laura , Leung Tiffany
Published: March 2020

SARS-CoV-2 has infected over 140,000 people as of March 14, 2020. We use a mathematical model to investigate the effectiveness of social distancing interventions lasting six weeks in a middle-sized city in the US. We explore four social distancing strategies by reducing the contacts of adults over 60 years old, adults over 60 years old and children, all adults (25, 75 or 95% compliance), and everyone in the population. Our results suggest that social distancing interventions can avert cases by 20% and hospitalizations and deaths by 90% even with modest compliance within adults as long as the intervention is kept in place, but the epidemic is set to rebound once the intervention is lifted. Our models suggest that social distancing interventions will buy crucial time but need to occur in conjunction with testing and contact tracing of all suspected cases to mitigate transmission of SARS-CoV-2.
Research on the Influence of Effective Distance Between Cities on the Cross-regional Transmission of COVID-19
Authors: Lin Shanlang , Qiao Yanning , Huang Junpei , Yan Na
Published: March 2020

The COVID-19 epidemic in China has been effectively controlled. It is of great significance to study the law of cross-regional spread of the epidemic, for the prevention and control of the COVID-19 in the future in China and other countries or regions. In this study, the cross-regional connection intensity between cities was characterized based on the probability and the effective distance of the shortest path tree, and the empirical analysis was carried out based on the high-frequency data such as the cases of COVID 19 outbreaks. It is concluded that the higher the intensity of inter-city connection, the larger scale the cross-regional spread of the epidemic.
When will the Covid-19 epidemic fade out?
Authors: Renna Ilaria
Published: March 2020

A discrete-time deterministic epidemic model is proposed with the aim of reproducing the behaviour observed in the incidence of real infectious diseases. For this purpose, we analyse a SIRS model under the framework of a small world network formulation. Using this model, we make predictions about the peak of the Covid-19 epidemic in Italy. A Gaussian fit is also performed, to make a similar prediction.
Elementary time-delay dynamics of COVID-19 disease
Authors: Menéndez José
Published: March 2020

An elementary model of COVID-19 dynamics—based on time-delay differential equations with a step-like survival function—is shown to be in good agreement with data from China and South Korea. The time-delal approach overcomes the major limitation of standard Susceptible-Exposed-Infected-Recovered (SEIR) models based on ordinary differential equations, namely their inability to predict the observed curve of infected individuals as a function of time. The model is also applied to countries where the epidemic is in earlier stages, such as Italy and Spain, to obtain estimates of the total number of cases and peak number of infected people that might be observed.
Basic prediction methodology for covid-19: estimation and sensitivity considerations
Authors: Britton Tom
Published: March 2020

The purpose of the present paper is to present simple estimation and prediction methods for basic quantities in an emerging epidemic like the ongoing covid-10 pandemic. The simple methods have the advantage that relations between basic quantities become more transparent, thus shedding light to which quantities have biggest impact on predictions, with the additional conclusion that uncertainties in these quantities carry over to high uncertainty also in predictions.

A simple non-parametric prediction method for future cumulative case fatalities, as well as future cumulative incidence of infections (assuming a given infection fatality risk f), is presented. The method uses cumulative reported case fatalities up to present time as input data. It is also described how the introduction of preventive measures of a given magnitude ρ will affect the two incidence predictions, using basic theory of epidemic models. This methodology is then reversed, thus enabling estimation of the preventive magnitude ρ, and of the resulting effective reproduction number RE. However, the effects of preventive measures only start affecting case fatalities some 3-4 weeks later, so estimates are only available after this time has elapsed. The methodology is applicable in the early stage of an outbreak, before, say, 10% of the community have been infected.

Beside giving simple estimation and prediction tools for an ongoing epidemic, another important conclusion lies in the observation that the two quantities f (infection fatality risk) and ρ (the magnitude of preventive measures) have very big impact on predictions. Further, both of these quantities currently have very high uncertainty: current estimates of f lie in the range 0.2% up to 2% ([9], [7]), and the overall effect of several combined preventive measures is clearly very uncertain.

The two main findings from the paper are hence that, a) any prediction containing f, and/or some preventive measures, contain a large amount of uncertainty (which is usually not acknowledged well enough), and b) obtaining more accurate estimates of in particular f, should be highly prioritized. Seroprevalence testing of random samples in a community where the epidemic has ended are urgently needed.
Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China
Authors: Yang Qihui , Yi Chunlin , Vajdi Aram , Cohnstaedt Lee W. , Wu Hongyu , Guo Xiaolong , Scoglio Caterina M.
Published: March 2020

As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.
Site-specific N-glycosylation Characterization of Recombinant SARS-CoV-2 Spike Proteins
Authors: Zhang Yong , Zhao Wanjun , Mao Yonghong , Chen Yaohui , Wang Shisheng , Zhong Yi , Su Tao , Gong Meng , Du Dan , Lu Xiaofeng , Cheng Jingqiu , Yang Hao
Published: March 2020

The glycoprotein spike (S) on the surface of SARS-CoV-2 is a determinant for viral invasion and host immune response. Herein, we characterized the site-specific N-glycosylation of S protein at the level of intact glycopeptides. All 22 potential N-glycosites were identified in the S-protein protomer and were found to be preserved among the 753 SARS-CoV-2 genome sequences. The glycosites exhibited glycoform heterogeneity as expected for a human cell-expressed protein subunits. We identified masses that correspond to 157 N-glycans, primarily of the complex type. In contrast, the insect cell-expressed S protein contained 38 N-glycans, primarily of the high-mannose type. Our results revealed that the glycan types were highly determined by the differential processing of N-glycans among human and insect cells. This N-glycosylation landscape and the differential N-glycan patterns among distinct host cells are expected to shed light on the infection mechanism and present a positive view for the development of vaccines and targeted drugs.
Government Responses Matter: Predicting Covid-19 cases in US using an empirical Bayesian time series framework
Authors: Liu Ziyue , Guo Wensheng
Published: March 2020

Since the Covid-19 outbreak, researchers have been predicting how the epidemic will evolve, especially the number in each country, through using parametric extrapolations based on the history. In reality, the epidemic progressing in a particular country depends largely on its policy responses and interventions. Since the outbreaks in some countries are earlier than United States, the prediction of US cases can benefit from incorporating the similarity in their trajectories. We propose an empirical Bayesian time series framework to predict US cases using different countries as prior reference. The resultant forecast is based on observed US data and prior information from the reference country while accounting for different population sizes. When Italy is used as prior in the prediction, which the US data resemble the most, the cases in the US will exceed 300,000 by the beginning of April unless strong measures are adopted.
Medical treatment of 55 patients with COVID-19 from seven cities in northeast China who fully recovered: a single-center, retrospective, observational study
Authors: Fan Lichao , Liu Huan , Li Na , Liu Chang , Gu Ye , Liu Yongyu , Chen Yu
Published: March 2020

Background

COVID-19 is an emerging disease caused by the SARS-CoV-2 virus; no specific medication has been identified to date. We aimed to investigate the administered medications and intervention times for patients who completely recovered from COVID-19.

Methods

This single-center, retrospective, and observational study included 55 patients with COVID-19 who were transferred to Shenyang Sixth People’s Hospital between January 20 and March 15, 2020. Demographic information, symptoms, laboratory indicators, treatment processes, and clinical outcomes were collected. Administered drugs and intervention times were compared in 47 and eight patients with mild and severe symptoms, respectively.

Findings

All 55 patients recovered. Fifty-three patients (96·36%) received antiviral therapy, including 45 in the mild group (median treatment: 14 days; 17 received umifenovir) and all eight severe-group patients (median treatment: 17·5 days; four received lopinavir/ritonavir). Twenty-nine patients (52·72%) were administered antibiotics, including 21 in the mild group (median treatment: 13·5 days; 15 received moxifloxacin) and all eight in the severe group (median treatment: 9 days; two received linezolid). Moreover, seven patients (12·72%) were treated with glucocorticoids and nine (16·36%) with immunomodulators.

Interpretation

Given the 100% recovery rate, early administration of antiviral drugs can be considered. Umifenovir may benefit patients with mild symptoms, while lopinavir/ritonavir may benefit those with severe symptoms. Prophylactic administration of common antibiotics may reduce the risk of co-infection. The use of glucocorticoids is usually not necessary.

Funding

This work was supported by the Shenyang Major Science and Technology Innovation R&D Program (JY2020-9-018 to Y. Chen).
Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT Scan Images
Authors: Fu Min , Yi Shuang-Lian , Zeng Yuanfeng , Ye Feng , Li Yuxuan , Dong Xuan , Ren Yan-Dan , Luo Linkai , Pan Jin-Shui , Zhang Qi
Published: March 2020

Purpose

COVID-19 has become global threaten. CT acts as an important method of diagnosis. However, human–based interpretation of CT imaging is time consuming. More than that, substantial inter-observer-variation cannot be ignored. We aim at developing a diagnostic tool for artificial intelligence (AI)–based classification of CT images for recognizing COVID-19 and other common infectious diseases of the lung.

Experimental Design

In this study, images were retrospectively collected and prospectively analyzed using machine learning. CT scan images of the lung that show or do not show COVID-19 were used to train and validate a classification framework based on convolutional neural network. Five conditions including COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary tuberculosis, and normal lung were evaluated. Training and validation set of images were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were collected from Zhongshan Hospital Xiamen University and the fifth Hospital of Wuhan.

Results

Accuracy, sensitivity, and specificity of the AI framework were reported. For test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively.

Conclusions

The performance of the proposed AI framework has excellent performance of recognizing COVID-19 and other common infectious diseases of the lung, which also has balanced sensitivity and specificity.
Which Measures are Effective in Containing COVID-19? — Empirical Research Based on Prevention and Control Cases in China
Authors: Lin Shanlang , Huang Junpei , He Ziwen , Zhan Dandan
Published: March 2020

Various epidemic prevention and control measures aimed at reducing person-to-person contact has paid a certain cost while controlling the epidemic. So accurate evaluation of these measures helps to maximize the effectiveness of prevention and control while minimizing social costs. In this paper, we develop the model in Dirk Brockmann and Dirk Helbing (2013) to theoretically explain the impact mechanism of traffic control and social distancing measures on the spread of the epidemic, and empirically tests the effect of the two measures in China at the present stage using econometric approach. We found that both traffic control and social distancing measures have played a very good role in controlling the development of the epidemic. Nationally, social distancing measures are better than traffic control measures; the two measures are complementary and their combined action will play a better epidemic prevention effect; Traffic control and social distancing do not work everywhere. Traffic control only works in cities with higher GDP per capita and population size, while fails in cities with lower GDP per capita and population size. In cities with lower population size, social distancing becomes inoperative; the rapid and accurate transmission of information, a higher protection awareness of the public, and a stronger confidence of residents in epidemic prevention can promote the realization of the measure effects. The findings above verify the effectiveness and correctness of the measures implemented in China at present, at the same time, we propose that it is necessary to fully consider the respective characteristics of the two measures, cooperating and complementing each other; what’s more, measures should be formulated according to the city’s own situation, achieving precise epidemic prevention; Finally, we should increase the transparency of information, improve protection awareness of the public, guide emotions of the public in a proper way, enhancing public confidence.
Early Estimation Of Reproduction Number of Covid-19 in Vietnam
Authors: Bui Long , Thanh Truong Nguyen , Ngoc Ha Nguyen
Published: March 2020

Reproduction number is an epidemiologic indicator that reflects the contagiousness and transmissibility of infectious agents. This paper aims to estimate the reproduction number of in the early phase of COVID-19 outbreak in Vietnam.
Herd immunity vs suppressed equilibrium in COVID-19 pandemic: different goals require different models for tracking
Authors: Huang Norden E. , Qiao Fangli , Qian Wang , Tung Ka-Kit
Published: March 2020

New COVID-19 epicenters have sprung up in Europe and US as the epidemic in China wanes. Many mechanistic models’ past predictions for China were widely off the mark (1, 2), and still vary widely for the new epicenters, due to uncertain disease characteristics. The epidemic ended in Wuhan, and later in South Korea, with less than 1% of their population infected, much less than that required to achieve “herd immunity”. Now as most countries pursue the goal of “suppressed equilibrium”, the traditional concept of “herd immunity” in epidemiology needs to be re-examined. Traditional model predictions of large potential impacts serve their purpose in prompting policy decisions on contact suppression and lockdown to combat the spread, and are useful for evaluating various scenarios. After imposition of these measures it is important to turn to statistical models that incorporate real-time information that reflects ongoing policy implementation and degrees of compliance to more realistically track and project the epidemic’s course. Here we apply such a tool, supported by theory and validated by past data as accurate, to US and Europe. Most countries started with a Reproduction Number of 4 and declined to around 1 at a rate highly dependent on contact-reduction measures.
A novel antiviral formulation inhibits a range of enveloped viruses
Authors: Fletcher Nicola F. , Meredith Luke W. , Tidswell Emma , Bryden Steven R , Gonçalves-Carneiro Daniel , Chaudhry Yasmin , Lowe Claire Shannon , Folan Michael A. , Lefteri Daniella A , Pingen Marieke , Bailey Dalan , McKimmie Clive S. , Baird Alan W.
Published: March 2020

Some free fatty acids derived from milk and vegetable oils are known to have potent antiviral and antibacterial properties. However, therapeutic applications of short to medium chain fatty acids are limited by physical characteristics such as immiscibility in aqueous solutions. We evaluated a novel proprietary formulation based on an emulsion of short chain caprylic acid, ViroSAL, for its ability to inhibit a range of viral infectionsin vitroandin vivo. In vitro, ViroSAL inhibited the enveloped viruses Epstein-Barr, measles, herpes simplex, Zika and orf parapoxvirus, together with Ebola, Lassa, vesicular stomatitis and SARS-CoV-1 pseudoviruses, in a concentration- and time-dependent manner. Evaluation of the components of ViroSAL revealed that caprylic acid was the main antiviral component; however, the ViroSAL formulation significantly inhibited viral entry compared with caprylic acid alone.In vivo, ViroSAL significantly inhibited Zika and Semliki Forest Virus replication in mice following the inoculation of these viruses into mosquito bite sites. In agreement with studies investigating other free fatty acids, ViroSAL had no effect on norovirus, a non-enveloped virus, indicating that its mechanism of action may be via surfactant disruption of the viral envelope. We have identified a novel antiviral formulation that is of great interest for prevention and/or treatment of a broad range of enveloped viruses.
Assessing the sex-related genomic composition difference using a K-mer-based approach: a case of study in Arapaima gigas (Pirarucu)
Authors: Cavalcante R.L.D , Ortega J.M. , Souza J.E.S , Sakamoto T.
Published: March 2020

Arapaima gigas is one of the largest freshwater bony fish in the world, in which adults could weigh 200 kilograms and measure 3 meters in length. Due to its large size and its low-fat meat, Arapaima gigas has quickly become a species of special interest in fish-farming. One challenge faced during their production is the lack of an efficient sexing methodology, since their sexual maturation occurs late (around the third to the fifth year) and the genetic mechanisms linked to their sex determination system are not known yet. For a more sustainable management, it is of paramount importance to seek an effective and non-invasive method to differentiate sexually juvenile individuals of Arapaima gigas. For this, the establishment of genetic markers associated with sexual differentiation would be an advantageous tool. In this study, we proposed a k-mer based approach to identify genome features with sex-determining properties. For this purpose, we used genomic data from four adult representatives of Arapaima gigas, two males and two females, and counted the k-mers comprising them. As result, we found k-mers from repetitive regions with high difference and disproportion in the count among individuals of the opposite sex. These differences in the k-mer-based genomic composition indicate the existence of genetic factors involved in the sexing of individuals in Arapaima gigas.
A kernel-based method to calculate local field potentials from networks of spiking neurons
Authors: Telenczuk Bartosz , Telenczuk Maria , Destexhe Alain
Published: March 2020

Background

The local field potential (LFP) is usually calculated from current sources arising from transmembrane currents, in particular in asymmetric cellular morphologies such as pyramidal neurons.

New method

Here, we adopt a different point of view and relate the spiking of neurons to the LFP through efferent synaptic connections and provide a method to calculate LFPs.

Results

We show that the so-called unitary LFPs (uLFP) provide the key to such a calculation. We show experimental measurements and simulations of uLFPs in neocortex and hippocampus, for both excitatory and inhibitory neurons. We fit a “kernel” function to measurements of uLFPs, and we estimate its spatial and temporal spread by using simulations of morphologically detailed reconstructions of hippocampal pyramidal neurons. Assuming that LFPs are the sum of uLFPs generated by every neuron in the network, the LFP generated by excitatory and inhibitory neurons can be calculated by convolving the trains of action potentials with the kernels estimated from uLFPs. This provides a method to calculate the LFP from networks of spiking neurons, even for point neurons for which the LFP is not easily defined. We show examples of LFPs calculated from networks of point neurons and compare to the LFP calculated from synaptic currents.

Conclusions

The kernel-based method provides a practical way to calculate LFPs from networks of point neurons.

Highlights

We provide a method to estimate the LFP from spiking neurons

This method is based on kernels, estimated from experimental data

We show applications of this method to calculate the LFP from networks of spiking neurons

We show that the kernel-based method is a low-pass filtered version of the LFP calculated from synaptic currents
LPV robust servo control of aircraft active side-sticks
Authors: Zhou Guang Rui , Liu Shi Qian , Sang Yuan Jun , Wang Xu Dong , Jia Xiao Peng , Niu Er Zhuo
Published: March 2020

Purpose

This paper aims to focus on the variable stick force-displacement (SFD) gradience in the active side stick (ASS) servo system for the civil aircraft.

Design/methodology/approach

The problem of variable SFD gradience was introduced first, followed by the analysis of its impact on the ASS servo system. To solve this problem, a linear-parameter-varying (LPV) control approach was suggested to process the variable gradience of the SFD. A H∞ robust control method was proposed to deal with the external disturbance.

Findings

To validate the algorithm performance, a linear time-variant system was calculated to be used to worst cases and the SFD gradience was set to linear and non-linear variation to test the algorithm, and some typical examples of pitch angle and side-slip angle tracking control for a large civil aircraft were also used to verify the algorithm. The results showed that the LPV control method had less settling time and less steady tracking errors than H∞ control, even in the variable SFD case.

Practical implications

This paper presented an ASS servo system using the LPV control method to solve the problem caused by the variable SFD gradience. The motor torque command was calculated by pressure and position feedback without additional hardware support. It was more useful for the electronic hydraulic servo actuator.

Originality/value

This was the research paper that analyzed the impact of the variable SFD gradience in the ASS servo system and presented an LPV control method to solve it. It was applicable for the SFD gradience changing in the linear and non-linear cases.
The effect of digital transformation strategy on performance
Authors: Wang Hecheng , Feng Junzheng , Zhang Hui , Li Xin
Published: March 2020

Purpose

The purpose of this study is to verify whether digital transformation strategy (DTS) could improve the organizational performance and provide a comprehensive analysis for enterprises on the necessity of implementing digital transformation in the context of China and draw on the perspectives of “Skewed conflict,” “minority dissent theory” and “too-much-of-a-good-thing.” This study investigates the curvilinear moderating role of cognitive conflict between DTS and performance.

Design/methodology/approach

An empirical investigation was used to collect a large sample data of Chinese enterprises’ digital transformation. A multiple linear regression analysis with SPSS was used to test the proposed hypotheses such as the inverted U-shaped moderating effect of the cognitive conflict.

Findings

In the Chinese context, DTS has a positive relationship on the short- and long-term financial performance. Moreover, this relationship was moderated by cognitive conflict such that the relationship between DTS and short-term financial performance could be further enhanced under the moderate cognitive conflict; however, the relationship between DTS and long-term financial performance was considerably influenced for higher cognitive conflict.

Originality/value

Based on the co-evolution of the information technology/information system (IT/IS) and business strategy, this study clarified the relationships among DTS, digital strategy and business and information technology strategies. By focusing on corporate strategy, this study further examined the effect of digital transformation on both short- and long-term financial performance. To further reveal the micro-psychological mechanisms underlying the effect of DTS on organizational performance, this study confirmed the inverted U-shaped moderating effect of the top management team’s cognitive conflict. Therefore, this research provides a new theoretical perspective for future research in the field of IT/IS, DTS and digital strategy.
Does bank competition matter for performance and risk-taking? empirical evidence from BRICS countries
Authors: Moudud-Ul-Huq Syed
Published: March 2020

Purpose

This study examines the relationship between banks' competition performance and risk-taking behavior concerning the impacts of bank size and the recent global financial crisis. The analysis empirically uses dynamic panel data from 1137 banks of the BRICS countries (i.e. Brazil Russia India China and South Africa) for the period 2000–2015.

Design/methodology/approach

Dynamic panel generalized method of moments (GMM) has been used primarily to examine the effect of bank competition on performance and risk-taking. Later the paper validates the core results by using three-stage least squares (3SLS) and incorporating alternative measure of competition in baseline equations.

Findings

This study confirms the significant impact of competition that complies with the structure-conduct-performance hypothesis quiet life hypothesis and “competition fragility” view. However, the key robust results are as follows: (1) in competitive markets large banks are more efficient than small banks; (2) there is a nonlinear relationship between competition performance and risk; (3) across bank size competition heterogeneously affects profitability efficiency risk and stability; (4) notably small banks are as efficient as large banks during crisis but shared with risk; and (5) small banks also stable during crisis in highly concentrated markets but less stable in competitive environments.

Practical implications

This study promotes higher market power for the bank's profitability and financial stability. More intently policymakers should nurture both cost and revenue efficiency for large banks as these are less efficient than small banks in concentrated markets though these banks produce risk. Hence those banks should be cautious to minimize non-performing loans and maximize stability regarding financial and efficiency. Based on the nonlinear pattern of competition the regulators should adopt different policies for short and long run. It also recommends encouraging commercial and cooperative banks in the BRICS region as these are more efficient risk-averse and better stabilized than other types of banks.

Originality/value

A good number of studies are available in the current literature which examines the impact of bank competition on either bank performance or risk-taking in a single country or cross country analysis. However, very few studies examine the relationship between bank performance and risk-taking behavior concerning the impacts of competition (non-linear and quadratic) size financial crisis and ownership structure together. Moreover, there is a dearth of literature on this topic that built on BRICS economies.