bluuebunny/crossref_metadata_2025_split huggingface dataset:
Distributions of LRS in varying environments
Authors: Tuljapurkar Shripad , Zuo Wenyun , Coulson Tim , Horvitz Carol , Gaillard Jean-Michel 
Published: June 2020

AbstractStudies of lifetime reproductive success (LRS) have shown that important random events can be in ecology and evolution. Randomness should be amplified in stochastic environments, and here we show here we show this to be the case by computing the complete distribution of LRS when vital rates are Markovian can be readily computed by building on our recent paper (Tuljapurkar et al. 2020). These results complement the work of van Daalen and Caswell (2020) on moments of LRS. We use empirical studies of Roe deer,Capreolus capreolus, to show that environments at birth have strong effects on future performance, and that analyses of the LRS in stochastic environments are a valuable element of studies of the consequences of climate change.
Positive interactions are common among culturable bacteria
Authors: Kehe Jared , Ortiz Anthony , Kulesa Anthony , Gore Jeff , Blainey Paul C. , Friedman Jonathan 
Published: June 2020

AbstractInterspecies interactions shape the structure and function of microbial communities. In particular, positive, growth-promoting interactions can significantly affect the diversity and productivity of natural and engineered communities. However, the prevalence of positive interactions and the conditions in which they occur are not well understood. To address this knowledge gap, we used kChip, an ultra-high throughput coculture platform, to measure 180,408 interactions among 20 soil bacteria across 40 carbon environments. We find that positive interactions, often described to be rare, occur commonly, primarily as parasitisms between strains that differ in their carbon consumption profiles. Notably, non-growing strains are almost always promoted by strongly growing strains (85%), suggesting a simple positive interaction-mediated approach for cultivation, microbiome engineering, and microbial consortium design.One Sentence SummaryExperimental measurement of >150,000 bacterial cocultures reveals that growth-promoting interactions occur commonly and depend on differences in nutrient consumption preferences.
Early cardiac inflammation as a driver of murine model of Arrhythmogenic Cardiomyopathy
Authors: Ng K.E. , Delaney P.J. , Thenet D. , Murtough S. , Webb C.M. , Tsisanova E. , Walker S.L.M , Westaby J.D. , Pennington D.J , Pink R. , Kelsell D.P. , Tinker A. 
Published: June 2020

AbstractThe study of a desmoglein 2 murine model of arrhythmogenic cardiomyopathy revealed cardiac inflammation as a key early event leading to fibrosis. Arrhythmogenic Cardiomyopathy (AC) is an inherited heart muscle disorder leading to ventricular arrhythmias and heart failure due to abnormalities in the cardiac desmosome. We examined how loss of desmoglein 2 (Dsg2) in the young murine heart leads to development of AC. Cardiomyocyte apoptosis was an early cellular phenotype and RNA-Seq analysis revealed early activation of inflammatory-associated pathways inDsg2null (Dsg2−/−) hearts at postnatal day 14 (Two weeks) that were absent in the fibrotic heart of adult mice (Ten weeks). This included upregulation of iRhom2/ADAM17 and its associated pro-inflammatory cytokines and receptors such as TNFα, IL6R and IL-6. Furthermore, genes linked to specific macrophage populations were upregulated. This suggests cardiomyocyte stress triggers an early immune response to clear apoptotic cells allowing tissue remodelling later on in the fibrotic heart. Our analysis at different disease stages implicate inflammation related to loss of desmoglein 2 as a major mechanism for disease progression.
Influenza Vaccination and COVID19 Mortality in the USA
Authors: Zanettini Claudio , Omar Mohamed , Dinalankara Wikum , Imada Eddie Luidy , Colantuoni Elizabeth , Parmigiani Giovanni , Marchionni Luigi 
Published: June 2020

ABSTRACTCOVID-19 mortality rate is higher in the elderly and in those with preexisting chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infection, and thus annual influenza vaccination is recommended for them.In this study, we explore a possible area-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data until June 10, 2020 together with population health data for the United States at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the number of deaths from COVID-19 as the outcome variable. We adjusted for a wide array of potential confounding variables using both county-level generalized propensity scores for influenza vaccination rates, as well as direct adjustment.Our results suggest that influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19. This finding is robust to using different analysis periods, different thresholds for inclusion of counties, and a variety of methodologies for confounding adjustment.In conclusion, our results suggest a potential protective effect of the influenza vaccine on COVID-19 mortality in the elderly population. The significant public health implications of this possibility point to an urgent need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level, to investigate both the epidemiology and any underlying biological mechanism.
Elevated levels of PM2.5 in crowded Subways of Cities with High COVID-19 related Mortality
Authors: Baron Yves Muscat 
Published: June 2020

BACKGROUNDRecent literature indicates that the pollutant, particulate matter PM2.5, may have an impact on COVID-19 related mortality. COVID-19 has been found adherent to PM2.5 and may be involved in the transmission and the exacerbation of COVID-19 infection, possibly due to PM2.5’s adverse influence on respiratory immunity. The PM2.5 levels in underground subways have been found up to 90 times higher than the surface levels. Moreover, the commuter congestion in the presence of such high levels of PM2.5 further encouraged COVID-19 human to human transmission.METHODThe levels of PM2.5 were retrieved from literature assessing particulate matter PM2.5 measured on subway platforms in two groups of cities. These cities were differentiated by the COVID-19 population percentage mortality rate (0.007% vs 0.09% (p<0.0004) the city’s population, more than a 10-fold difference. Data regarding the number of stations, length of the networks (km) and the annual ridership were also obtained from literature related to underground commuting.RESULTSThe population percentage mortality related to COVID-19 infection correlated significantly for both minimum (p<0.01) and maximum (p<0.00001) levels of PM2.5. The cities’ subways with low COVID-19 mortality had minimum platform PM2.5 levels of 27.4 (SD+/-17.2µg/m3) compared to 63.4µg/m3 (SD+/-10.8µg/m3) in cities with high COVID-19 associated mortality (p<0.01). Subway maximum levels of PM2.5 in cities with low COVID-19 mortality was 53.4µg/m3 (SD+/- 21.8µg/m3) while that of underground networks with high COVID-19 mortality had maximum platform PM2.5 levels of 172.1µg/m3 (SD+/-98µg/m3) (p<0.001). The cities with higher COVID-19 mortality had longer networks 230km (SD+/-111km) versus 119km (SD+/-99km) (p<0.03) and more stations 191 (SD+/-109) versus 102 (SD+/-94) (p<0.047). Although the annual ridership in the cities with the high COVID-19 mortality was higher (1034×106 vs 751×106) this did not achieve statistical significance. The maximum PM2.5 correlated with the number of stations (p<0.045) and the length of the networks (p< 0.044). The minimum PM2.5 did not achieve similar significant correlations to subway variables. Ridership significantly correlated with number of stations (p<0.01) and the length of the network (p<0.02).CONCLUSIONUnderground networks may have inherent characteristics accelerating spread of COVID-19 infection and consequent mortality. The highly elevated levels of PM2.5 in overcrowded subways with extensive reach may have acted as a co-factor to disseminate the pandemic.
A Multi-Task Pipeline with Specialized Streams for Classification and Segmentation of Infection Manifestations in COVID-19 Scans
Authors: EL-Bana Shimaa , Al-Kabbany Ahmad , Sharkas Maha 
Published: June 2020

AbstractWe are concerned with the challenge of coronavirus disease (COVID-19) detection in chest X-ray and Computed Tomography (CT) scans, and the classification and segmentation of related infection manifestations. Even though it is arguably not an established diagnostic tool, using machine learning-based analysis of COVID-19 medical scans has shown the potential to provide a preliminary digital second opinion. This can help in managing the current pandemic, and thus has been attracting significant research attention. In this research, we propose a multi-task pipeline that takes advantage of the growing advances in deep neural network models. In the first stage, we fine-tuned an Inception-v3 deep model for COVID-19 recognition using multi-modal learning, i.e., using X-ray and CT scans. In addition to outperforming other deep models on the same task in the recent literature, with an attained accuracy of 99.4%, we also present comparative analysis for multi-modal learning against learning from X-ray scans alone. The second and the third stages of the proposed pipeline complement one another in dealing with different types of infection manifestations. The former features a convolutional neural network architecture for recognizing three types of manifestations, while the latter transfers learning from another knowledge domain, namely, pulmonary nodule segmentation in CT scans, to produce binary masks for segmenting the regions corresponding to these manifestations. Our proposed pipeline also features specialized streams in which multiple deep models are trained separately to segment specific types of infection manifestations, and we show the significant impact that this framework has on various performance metrics. We evaluate the proposed models on widely adopted datasets, and we demonstrate an increase of approximately 4% and 7% for dice coefficient and mean intersection-over-union (mIoU), respectively, while achieving 60% reduction in computational time, compared to the recent literature.
Determination of Robust Regional CT Radiomics Features for COVID-19
Authors: Tamal Mahbubunnabi 
Published: June 2020

ABSTRACTBackgroundThe lung CT images of COVID-19 patients can be characterized by three different regions – Ground Glass Opacity (GGO), consolidation and pleural effusion. GCOs have been shown to precede consolidations. Quantitative characterization of these regions using radiomics can facilitate accurate diagnosis, disease progression and response to treatment. However, according to the knowledge of the author, regional CT radiomics analysis of COVID-19 patients has not been carried out. This study aims to address these by determining the radiomics features that can characterize each of the regions separately and can distinguish the regions from each other.Methods44 radiomics features were generated with four quantization levels for 23 CT slice of 17 patients. Two approaches were the implemented to determine the features that can differentiate between lung regions – 1) Z-score and correlation heatmaps and 2) one way ANOVA for finding statistically significantly difference (p<0.05) between the regions. Radiomics features that show agreement for all cases (Z-score, correlation and statistical significant test) were selected as suitable features. The features were then tested on 52 CT images.Results10 radiomics features were found to be the most suitable among 44 features. When applied on the test images, they can differentiate between GCO, consolidation and pleural effusion successfully and the difference provided by these 10 features between three lung regions are statistically significant.ConclusionThe ten robust radiomics features can be useful in extracting quantitative data from CT lung images to characterize the disease in the patient, which in turn can help in more accurate diagnosis, staging the severity of the disease and allow the clinician to plan for more successful personalized treatment for COVID-19 patients. They can also be used for monitoring the progression of COVID-19 and response to therapy for clinical trials.
Modelling lockdown-induced 2ndCOVID waves in France
Authors: Daunizeau Jean , Moran Rosalyn , Brochard Jules , Mattout Jérémie , Frackowiak Richard , Friston Karl 
Published: June 2020

AbstractAs with the Spanish Flu a century ago, authorities have responded to the current COVID-19 pandemic with extraordinary public health measures. In particular, lockdown and related social distancing policies are motivated in some countries by the need to slow virus propagation—so that the primary wave of patients suffering from severe forms of COVID infection do not exceed the capacity of intensive care units. But unlocking poses a critical issue because relaxing social distancing may, in principle, generate secondary waves. Ironically however, the dynamic repertoire of established epidemiological models that support this kind of reasoning is limited to single epidemic outbreaks. In turn, predictions regarding secondary waves are tautologically derived from imposing assumptions about changes in the so-called “effective reproduction number”. In this work, we depart from this approach and extend the LIST (Location-Infection-Symptom-Testing) model of the COVID pandemic with realistic nonlinear feedback mechanisms that under certain conditions, cause lockdown-induced secondary outbreaks. The original LIST model captures adaptive social distancing,i.e. the transient reduction of the number of person-to-person contacts (and hence the rate of virus transmission), as a societal response to salient public health risks. Here, we consider the possibility that such pruning of socio-geographical networks may also temporarily isolate subsets of local populations from the virus. Crucially however, such unreachable people will become susceptible again when adaptive social distancing relaxes and the density of contacts within socio-geographical networks increases again. Taken together, adaptive social distancing and networkunreachabilitythus close a nonlinear feedback loop that endows the LIST model with a mechanism that can generate autonomous (lockdown-induced) secondary waves. However, whether and how secondary waves arise depend upon the interaction with other nonlinear mechanisms that capture other forms of transmission heterogeneity. We apply the ensuing LIST model to numerical simulations and exhaustive analyses of regional French epidemiological data. In brief, we find evidence for this kind of nonlinear feedback mechanism in the empirical dynamics of the pandemic in France. However, rather than generating catastrophic secondary outbreaks (as is typically assumed), the model predicts that the impact of lockdown-induced variations in population susceptibility and transmission may eventually reduce to a steady-state endemic equilibrium with a low but stable infection rate.
Estimating COVID-19 under-reporting across 86 nations: implications for projections and control
Authors: Rahmandad Hazhir , Lim Tse Yang , Sterman John 
Published: June 2020

AbstractCOVID-19 prevalence and mortality remain uncertain. For all 86 countries with reliable testing data we estimate how asymptomatic transmission, disease acuity, hospitalization, and behavioral responses to risk shape pandemic dynamics. Estimated cumulative cases and deaths through 10 July 2020 are 10.5 and 1.47 times official reports, yielding an infection fatality rate (IFR) of 0.65%, with wide variation across nations. Despite underestimation, herd immunity remains distant. Sufficient early testing could have averted 39.7 (35.3-45.3) million cases and 218 (191-257) thousand deaths. Responses to perceived risk cause the reproduction number to settle near 1, but with very different steady-state incidence, while some nations experience endogenous rebounds. Scenarios through March 2021 show modest enhancements in responsiveness could reduce cumulative cases ≈80%, to 271 (254-412) million across these nations.One Sentence SummaryCOVID-19 under-reporting is large, varies widely across nations, and strongly conditions projected outbreak dynamics.
Characterization of a novel, low-cost, scalable ozone gas system for sterilization of N95 respirators and other COVID-19 related use cases.
Authors: Dave Nikhil , Pascavis Katie Sue , Patterson John , Kozicki Michael , Wallace David , Chowdhury Abhik , Abbaszadegan Morteza , Alum Absar , Herckes Pierre , Zhang Zhaobo , Chang Josh , Ewell Clinton , Smith Tyler , Naufel Mark 
Published: June 2020

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an elusive and highly pathogenic agent, has resulted in the ongoing COVID-19 pandemic affecting numerous populations worldwide. New studies investigating the tenacity of SARS-CoV-2 have highlighted its ability to persist on a myriad of surfaces for several days, including gowns and shoes. As a result, there is a global need for sterilization of a variety of potentially-contaminated items, ranging from clothing to personal protective equipment like face coverings. To this end, we have designed and constructed a cost-effective, scalable, and sustainable sterilization system that uses ozone gas to inactivate viral particles. We sought to determine the efficacy of the system in the sterilization of viral particles as well as its ability to sterilize N95 respirators for reuse. N95 respirators inoculated with P22 bacteriophage and sterilized in the ozone system showed a 6-log10 reduction in viral load when treated at 25 ppm for 150 minutes. Further, N95 respirators treated with five 150-minute cycles at 35 ppm for a total concentration-time product (CT) of 26,250 ppm min in the ozone system showed comparable filtration efficiency to untreated N95 respirators in a 50 to 200 nmr particulate challenge filtration test. Interestingly, the surgical N95 respirators tested showed complete inactivation of fluid resistance and degradation of the elasticity of polyisoprene straps after five cycles in the sterilization system. Taken together, these data suggest that while our ozone system may negatively affect certain protective aspects of surgical N95 respirators, it does effectively sterilize viral particles and can be utilized for a multitude of other use cases, including sterilizing polypropylene face coverings after potential SARS-CoV-2 contamination. In addition to providing long-term environmental benefits, deployment of this system during the ongoing pandemic reduces the risk of COVID-19 community transmission while conserving monetary resources otherwise spent on the continuous purchase of disposable face coverings.
Shut and re-open: the role of schools in the spread of COVID-19 in Europe
Authors: Stage Helena B. , Shingleton Joseph , Ghosh Sanmitra , Scarabel Francesca , Pellis Lorenzo , Finnie Thomas 
Published: June 2020

AbstractWe investigate the effect of school closure and subsequent reopening on the transmission of COVID-19, by considering Denmark, Norway, Sweden, and German states as case studies. By comparing the growth rates in daily hospitalisations or confirmed cases under different interventions, we provide evidence that school closures contribute to a reduction in the growth rate approximately 7 days after implementation. Limited school attendance, such as older students sitting exams or the partial return of younger year groups, does not appear to significantly affect community transmission. In countries where community transmission is generally low, such as Denmark or Norway, a large-scale reopening of schools while controlling or suppressing the epidemic appears feasible. However, school reopening can contribute to statistically significant increases in the growth rate in countries like Germany, where community transmission is relatively high. In all regions, a combination of low classroom occupancy and robust test-and-trace measures were in place. Our findings underscore the need for a cautious evaluation of reopening strategies.
Evaluating aerosol and splatter following dental procedures: addressing new challenges for oral healthcare and rehabilitation
Authors: Allison James R , Currie Charlotte C , Edwards David C , Bowes Charlotte , Coulter Jamie , Pickering Kimberley , Kozhevnikova Ekaterina , Durham Justin , Nile Christopher J , Jakubovics Nicholas , Rostami Nadia , Holliday Richard 
Published: June 2020

AbstractBackgroundDental procedures often produce aerosol and splatter which are potentially high risk for spreading pathogens such as SARS-CoV-2. The existing literature is limited.Objective(s)To develop a robust, reliable and valid methodology to evaluate distribution and persistence of dental aerosol and splatter, including the evaluation of clinical procedures.MethodsFluorescein was introduced into the irrigation reservoirs of a high-speed air-turbine, ultrasonic scaler and 3-in-1 spray and procedures performed on a mannequin in triplicate. Filter papers were placed in the immediate environment. The impact of dental suction and assistant presence were also evaluated. Samples were analysed using photographic image analysis, and spectrofluorometric analysis. Descriptive statistics were calculated and Pearson’s correlation for comparison of analytic methods.ResultsAll procedures were aerosol and splatter generating. Contamination was highest closest to the source, remaining high to 1-1.5 m. Contamination was detectable at the maximum distance measured (4 m) for high-speed air-turbine with maximum relative fluorescence units (RFU) being: 46,091 at 0.5 m, 3,541 at 1.0 m, and 1,695 at 4 m. There was uneven spatial distribution with highest levels of contamination opposite the operator. Very low levels of contamination (≤0.1% of original) were detected at 30 and 60 minutes post procedure. Suction reduced contamination by 67-75% at 0.5-1.5 m. Mannequin and operator were heavily contaminated. The two analytic methods showed good correlation (r=0.930, n=244, p<0.001).ConclusionDental procedures have potential to deposit aerosol and splatter at some distance from the source, being effectively cleared by 30 minutes in our setting.
High-Throughput Kinase Inhibitor Screening Reveals Roles for Aurora and Nuak Kinases in Neurite Initiation and Dendritic Branching
Authors: Blazejewski Sara M. , Bennison Sarah A. , Liu Xiaonan , Toyo-oka Kazuhito 
Published: June 2020

AbstractKinases are essential regulators of a variety of cellular signaling processes, including neurite formation – a foundational step in neurodevelopment. Aberrant axonal sprouting and failed regeneration of injured axons are associated with conditions like traumatic injury, neurodegenerative disease, and seizures. Investigating the mechanisms underlying neurite formation will allow for identification of potential therapeutics. We used a kinase inhibitor library to screen 493 kinase inhibitors and observed that 45% impacted neuritogenesis in Neuro2a (N-2a) cells. Based on the screening, we further investigated the roles of Aurora kinases A, B, and C and Nuak kinases 1 and 2. The roles of Aurora and Nuak kinases have not been thoroughly studied in the nervous system. Inhibition or overexpression of Aurora and Nuak kinases in primary cortical neurons resulted in various neuromorphological defects, with Aurora A regulating neurite initiation, Aurora B and C regulating neurite initiation and elongation, all Aurora kinases regulating arborization, and all Nuak kinases regulating neurite initiation and elongation and arborization. Our high-throughput screening and analysis of Aurora and Nuak kinases revealed their functions and may contribute to the identification of therapeutics.