Initial Growth Rate Research Articles

Inferring generation-interval distributions from contact-tracing data.

Generation intervals, defined as the time between when an individual is infected and when that individual infects another person, link two key quantities that describe an epidemic: the initial reproductive number, , and the initial rate of exponential growth, r. Generation intervals can be measured through contact tracing by identifying who infected whom. We study how realized intervals differ from ‘intrinsic’ intervals that describe individual-level infectiousness and identify both spatial and temporal effects, including truncating (due to observation time), and the effects of susceptible depletion at various spatial scales. Early in an epidemic, we expect the variation in the realized generation intervals to be mainly driven by truncation and by the population structure near the source of disease spread; we predict that correcting realized intervals for the effect of temporal truncation but not for spatial effects will provide the initial forward generation-interval distribution, which is spatially informed and correctly links r and . We develop and test statistical methods for temporal corrections of generation intervals, and confirm our prediction using individual-based simulations on an empirical network.

COVID-19 UK Lockdown Forecasts and R 0.

Introduction: The first reported UK case of COVID-19 occurred on 30 January 2020. A lockdown from 24 March was partially relaxed on 10 May. One model to forecast disease spread depends on clinical parameters and transmission rates. Output includes the basic reproduction number R0 and the log growth rate r in the exponential phase.Methods: Office for National Statistics data on deaths in England and Wales is used to estimate r. A likelihood for the transmission parameters is defined from a gaussian density for r using the mean and standard error of the estimate. Parameter samples from the Metropolis-Hastings algorithm lead to an estimate and credible interval for R0 and forecasts for cases and deaths.Results: The UK initial log growth rate is r = 0.254 with s.e. 0.004. R0 = 6.94 with 95% CI (6.52, 7.39). In a 12 week lockdown from 24 March with transmission parameters reduced throughout to 5% of their previous values, peaks of around 90,000 severely and 25,000 critically ill patients, and 44,000 cumulative deaths are expected by 16 June. With transmission rising from 5% in mid-April to reach 30%, 50,000 deaths and 475,000 active cases are expected in mid-June. Had such a lockdown begun on 17 March, around 30,000 (28,000, 32,000) fewer cumulative deaths would be expected by 9 June.Discussion: The R0 estimate is compatible with some international estimates but over twice the value quoted by the UK government. An earlier lockdown could have saved many thousands of lives.

New particle formation and sub-10 nm size distribution measurements during the A-LIFE field experiment in Paphos, Cyprus

Abstract. Atmospheric particle size distributions were measured in Paphos, Cyprus, during the A-LIFE (absorbing aerosol layers in a changing climate: ageing, lifetime and dynamics) field experiment from 3 to 30 April 2017. The newly developed differential mobility analyser train (DMA-train) was deployed for the first time in an atmospheric environment for the direct measurement of the nucleation mode size range between 1.8 and 10 nm diameter. The DMA-train set-up consists of seven size channels, of which five are set to fixed particle mobility diameters and two additional diameters are obtained by alternating voltage settings in one DMA every 10 s. In combination with a conventional mobility particle size spectrometer (MPSS) and an aerodynamic particle sizer (APS) the complete atmospheric aerosol size distribution from 1.8 nm to 10 µm was covered. The focus of the A-LIFE study was to characterize new particle formation (NPF) in the eastern Mediterranean region at a measurement site with strong local pollution sources. The nearby Paphos airport was found to be a large emission source for nucleation mode particles, and we analysed the size distribution of the airport emission plumes at approximately 500 m from the main runway. The analysis yielded nine NPF events in 27 measurement days from the combined analysis of the DMA-train, MPSS and trace gas monitors. Growth rate calculations were performed, and a size dependency of the initial growth rate (<10 nm) was observed for one event case. Fast changes of the sub-10 nm size distribution on a timescale of a few minutes were captured by the DMA-train measurement during early particle growth and are discussed in a second event case. In two cases, particle formation and growth were detected in the nucleation mode size range which did not exceed the 10 nm threshold. This finding implies that NPF likely occurs more frequently than estimated from studies where the lower nanometre size regime is not covered by the size distribution measurements.

Effect of single amino acid addition on growth kinetics and flavor modulation by Torulaspora delbrueckii in soy (tofu) whey alcoholic beverage fermentation

Soy (tofu) whey is a by-product commonly disposed of by tofu manufacturers around the world. Due to its nutritious nature, direct disposal of soy whey into the sewage can result in a detrimental impact on the environment in the long-run. In this study, soy whey supplemented with four individual amino acids (valine, leucine, isoleucine and phenylalanine) equivalent to 120 mg N/L was fermented with a yeast Torulaspora delbrueckii Biodiva. The supplementation of an individual amino acid resulted in faster sugar utilization and lower levels of residual sugar than the unsupplemented whey but did not result in a significantly higher amount of ethanol (7–8% v/v) at the end of fermentation. Isoleucine supplementation resulted in a slightly slower initial yeast growth rate when compared to the control while the other three amino acids had identical yeast growth kinetics to the control. Isoleucine supplementation also resulted in slower sugar utilization during the first four days. Therefore, isoleucine is least preferred by the yeast. The supplementation of amino acids resulted in greater formation of higher alcohols and their corresponding alcohol-derived esters. Overall, the supplementation of a single amino acid enhanced sugar utilization and impacted flavor compounds of the resultant soy whey alcoholic beverage.

In silico mouse model of infection and immunity

Abstract An organism’s immune system tries to protect it by identifying the presence of pathogens and attempting to eliminate them. The defense is twofold: innate immune cells mobilize rapidly, while acquired immune cells slowly develop into pathogen-killing specialists. These responses incur collateral tissue damage, which anti-inflammatory mediators seek to control. This system of checks and balances is responsible for host survival. Experimental research has demonstrated how vastly complex these interactions are, indicating a place for theoretical and computational study. In this work we develop a comprehensive differential equation model of the immune system by considering interactions between immune system components in the presence of pathogen or tissue trauma. Through this step-by-step construction we explore the dependence of the anti-inflammatory mediators on pathogen levels, and also how they temper the immune response at the end of infection. We then challenge the “virtual mouse” with typical pathogens of varying virulence and observe the outcomes via model simulation. We find that anti-inflammation can downregulate the activation and proliferation of immune cells or promote apoptosis as cessation mechanisms, suggesting the need for in vivo experiments. Bifurcation theory describes how the outcomes of infection depend on model parameters, from which we conclude that initial insult and pathogen growth rate allow us to predict whether or not the in silico mouse overcomes the disease in a deterministic framework.

Development of academic vocabulary across primary school age: Differential growth and influential factors for German monolinguals and language minority learners.

Academic language has been shown to significantly contribute to success across school subjects. However, to date, there are no empirical studies addressing its development across primary school age. The present study investigated the growth of academic vocabulary and influential conditions from Grades 2 to 4 based on a newly developed and validated test instrument. Drawing on a German sample of 472 children (N = 281 language minority learners) we found evidence of a Matthew effect with the group of language minority learners showing both lower initial level and lower growth rates compared to their monolingual peers. In addition, results of simple and full conditional growth curve models underline the significant impact of family background (parental education, books at home) and of children's nonverbal cognitive abilities on the intercept as well as the growth of academic vocabulary, thereby supporting the assumption of an additional Matthew effect according to family background, particularly in monolingual majority language children. Our results indicate the need for an early promotion of the register of academic language in monolingual as well as language minority students. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Examining Factors Affecting Reading and Math Growth and Achievement Gaps in Grades 1-5: A Cohort-Sequential Longitudinal Approach.

Despite focused efforts, achievement gaps remain a problem in the America's education system, especially those between students from higher and lower income families. Continued work on reducing these gaps benefits from an understanding of students' reading and math growth from typical school instruction and how growth differs based on initial proficiency, grade, and demographic characteristics. Data from 5,900 students in Grades 1-5 tested in math and reading at six points across two years were analyzed using cohort-sequential latent growth curve models to determine longitudinal growth patterns. Results indicated that students with low initial proficiency grew more quickly than students with higher proficiency. However, after two school years their achievement remained below average and well below that of students with higher initial proficiency. Demographic characteristics had small but significant effects on initial score and growth rates.

Quantifying the antiviral effect of APOBEC3 on HIV-1 infection in humanized mouse model

APOBEC3 proteins inhibit human immunodeficiency virus (HIV)-1 infection by independently impairing viral reverse transcription and inducing G-to-A mutations in viral DNA. An HIV-1-encoded protein, viral infectivity factor (Vif), can counteract these antiviral activities of APOBEC3 proteins. Although previous studies using in vitro cell culture systems have revealed the molecular mechanisms of the antiviral action of APOBEC3 proteins and their antagonism by Vif, it remains unclear how APOBEC3 proteins affect the kinetics of HIV-1 replication in vivo. Here we quantified the time-series of viral load datasets from humanized mice infected with HIV-1 variants in the presence of APOBEC3F, APOBEC3G, or both APOBEC3F/G using a simple mathematical model that accounted for inter-individual variability. Through experimental and mathematical investigation, we formulated and calculated the total antiviral activity of APOBEC3F and APOBEC3G based on the estimated initial growth rates of viral loads in vivo. Interestingly, we quantitatively demonstrated that compared with APOBEC3G, the antiviral activity of APOBEC3F was widely distributed but skewed toward lower activity, although their mean values were similar. We concluded that APOBEC3G markedly and robustly restricted the initial stages of viral growth in vivo. This is the first report to quantitatively elucidate how APOBEC3F and APOBEC3G differ in their anti-HIV-1 modes in vivo.

High speed PLIF study of the Richtmyer–Meshkov instability upon re-shock

The Richtmyer–Meshkov instability (RMI) of a twice-shocked gas interface is studied using high-speed planar-laser induced fluorescence (PLIF) in the Wisconsin Shock Tube Laboratory’s vertical shock tube. The initial condition (IC) is a shear layer with broadband diffuse perturbations at the interface between a helium-acetone mixture and argon. This IC is accelerated by a shock of nominal strength M=1.8, and then accelerated again by the transmitted shock that reflects off the end wall of the tube. An estimate of the light gas mole fraction is extracted from high-speed imaging using an iterative process that accounts for the nonlinear temperature dependence of the acetone’s fluorescence quantum yield (FQY) and absorption cross-section. A vorticity deposition model for the initial growth rate after re-shock is compared with the Mikaelian model for re-shock. Previously used in literature, the number of generations is shown to naturally arise from a normalisation of the scalar transport equation. A self-similar analysis is then performed using the mole fraction data to explore the evolution of the RMI after reshock and the higher order moments of the light gas mole fraction are compared with a proposed model.

Number of COVID-19 cases in Chile at 120 days with data at 21/03/2020 and threshold of daily effort to flatten the epi-curve.

We present a straightforward projection with data up to 21/03/2020 of the evolution of the number of COVID-19 cases per day in Chile using data from the Ministry of Health. Assuming an arithmetical growth in the second variation of the data, we present a cubic adjustment model in which we estimate over 100 000 cases at 120 days consistent with the data recorded to date. Furthermore, we use an exponential total case model to represent (using a parameter) the daily effort to reduce a high initial daily growth rate. We simulate this model with different numerical scenarios of feasibility and desired future prevalence.

Application of high-throughput plant phenotyping for assessing biophysical traits and drought response in two oak species under controlled environment

Early plant selection for desirable traits is important in tree improvement programs and sustainable forest management. In this study, we demonstrate the use of image based high-throughput plant phenotyping (HTPP, LemnaTec 3D Scanalyzer, Germany), with Red, Green, Blue (RGB), and hyperspectral cameras, to quantify Quercus bicolor and Quercus prinoides seedlings growth and development [plant height, projected leaf area (LA), plant/canopy width, ConvexHull, and plant aspect ratio], and assess their response to a dry-down period, under controlled environment. HTPP images were validated against low throughput measurements, including gas exchange, leaf spectral properties, and morphological traits. Using HTPP, we recorded significant differences in growth dynamic in examined species, with faster initial growth rate early in the growing season, higher photosynthetic rates, larger LA, and seedling dimension, in Q. bicolor, compared to Q. prinoides. This has ecological implications on species responses to shading and timing of drought stress, as well as their competitive relationship with each other and with other species under changing climate. HTTP showed that both growth and leaf expansion ceased under dry-down treatment. Image derived and measured morphological traits were highly and significantly correlated under both well-watered and dry-down conditions for both species. To obtain meaningful physiological information using spectrometry, we calculated 12 vegetation indices (VIs) from both HTPP and handheld spectrometers. Vogelmann and Maccioni indices had the highest correlations across methods, suggesting their potential use for assessing oak seedlings performance and health. Our results emphasized the importance of VIs ground truthing, since VIs performance can vary significantly between species and treatments. HTPP tools can successfully be used to effectively assess forest seedlings of the two Quercus species, important for early plant selection for forest management purposes and tree improvement programs.

Impact of biodegradation of oil on the kinetics of gas hydrate formation and decomposition

Nowadays the biological methods based on utilization of bacteria for different purposes in oilfields are becoming more and more popular because these methods potentially may become a cheaper alternative to chemical treatments. Gas hydrates formation in pipelines is the one of the main flow assurance problems. In previous works it has been shown that some biodegraded oils can have components that may act as natural Anti-Agglomerants. These natural Anti-Agglomerants can prevent hydrate particles from agglomeration which allow the particles to flow downstream along with oil in form of slurry. But there is lack of understanding of how products of oil biodegradation affect the kinetic aspects of hydrate nucleation, formation and decomposition. This information might be useful for development of new approached of treatment of the hydrate related issues in oil pipelines.Impact of the changes in component composition of crude oil caused by biodegradation process on the kinetics of gas hydrates nucleation, formation and decomposition has been studied with different methods. It was demonstrated that the changes of chemical composition of crude oil caused by biodegradation can have a strong impact on the hydrate nucleation rates at static conditions. Almost twofold decrease of hydrate nucleation rate was observed in the experiments with the water-in-oil emulsion for the biodegraded oils compared to one obtained with emulsion in the original crude oil. At the same time, no significant effect of biodegradation on the hydrate film growth rates at static conditions was observed. Similar tangential and normal hydrate film growth rates were obtained in experiments with both crude oil and with biodegraded oil. Initial growth rates of hydrate in stirring emulsions were an order of magnitude higher for water emulsions in the biodegraded oil compared to ones for the original crude oil. The studies of the decomposition rates of hydrates at sub-zero temperatures and at dynamic conditions showed that the products of vital activity of bacteria promoted self-preservation of hydrates dispersed in the biodegraded oil.

Canada Needs to Rapidly Escalate Public Health Interventions for Its COVID-19 Mitigation Strategies

Background. After the declaration of COVID-19 pandemic on March 11th 2020, local transmission chains starting in different countries including Canada are forcing governments to take decisions on public health interventions to mitigate the spread of the epidemic. Methods. We conduct data-driven and model-free estimations for the growth rates of the COVID-19 epidemics in Italy and Canada, by fitting an exponential curve to the daily reported cases. We use these estimates to predict epidemic trends in Canada under different scenarios of public health interventions. Results. In Italy, the initial growth rate (0.22) has reduced to 0.1 two weeks after the lockdown of the country on March 8th 2020. This corresponds to a reduction of the doubling time from about 3.15 to almost 7 days. In comparison, the growth rate in Canada has increased from 0.13 between March 1st and 13th, to 0.25 between March 13th to 22nd. This current growth rate corresponds to a doubling time of 2.7 days, and therefore, unless further public health interventions are escalated in Canada, we project 15,000 cases by March 31st. However, the case number can be reduced to 4,000 if escalated public health interventions can be implemented instantly to reduce the growth rate to 0.1, the same level achieved in Italy. Interpretation. Intervention measures implemented so far in different countries worldwide have been effective in reducing the growth rate and increasing the doubling time, but their effects come with a substantial delay as long as 2 weeks. Prompt and farsighted interventions are critical to counteract the very rapid initial growth of the COVID-19 epidemic in Canada. Mitigation plans must take into account the delayed effect of interventions by up to 2-weeks and the short doubling time of 3-4 days.

Initial axon growth rate from embryonic sensory neurons is correlated with birth date

Axon growth rate from different populations of sensory neurons is correlated with the distance they have to grow to reach their targets in development: neurons with more distant targets extend axons at intrinsically faster rates. With growth of the embryo, later‐born neurons within each population have further to extend their axons to reach their targets than early‐born neurons. Here we examined whether the axon growth rate is related to birth date by studying the axon growth from neurons that differentiate in vitro from precursor cells isolated throughout the period of neurogenesis. We first showed that neurons that differentiated in vitro from different precursor cell populations exhibited differences in axon growth rate related to in vivo target distance. We then examined the axon growth rate from neurons that differentiate from the same precursor population at different stages throughout the period of neurogenesis. We studied the epibranchial placode precursors that give rise to nodose ganglion neurons in the chicken embryo. We observed a highly significant, threefold difference in axon growth rate from neurons that differentiate from precursor cells cultured early and late during the period of neurogenesis. Our findings suggest that intrinsic differences in axon growth rate are correlated with the neuronal birth date.

Mathematical Modeling of Exhaustion Process of Extracted Mineral Resources Using the Example of Natural Uranium

This paper is devoted to the development of mathematical models of exhaustion process of extracted mineral resources. Our new model combines the advantages of previous models and is devoid of their disadvantages. The model uses the transition of the growth rate of extraction of mineral resource to stationary value. The initial known parameters of the model include the explored residual volume of the mineral resource, the initial volume of its extraction, the initial growth rate of its extraction, and the indicator of decreasing rate of extraction growth. The model is applied using the example of predicted extraction and exhaustion of natural uranium.

Early prediction of antigenic transitions for influenza A/H3N2.

Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection.

Strength of Cement Mortar Solidified Soil and Prediction

Strength is an important index to evaluate the solidification effect of cement soil and engineering design and construction inspection. The unconfined compressive strength test of cement mortar solidified soil was carried out. The results showed that the strength growth rate of cement mortar solidified soil increased linearly with the sand content, which was the maximum at 7d, and tended to be stable after 14d. The strength of solidified soil increased with age, and the initial growth rate was relatively fast. Compared with cement soil, the coefficient of deformation of cement mortar solidified soil did not slow down obviously in the later period. Taking cement mortar solidified soil as cement soil for strength calculation, the strength prediction equation of cement solidified soft soil at full age could well reflect the strength growth rule of cement mortar solidified soil.

Gas hydrate formation probability and growth rate as a function of kinetic hydrate inhibitor (KHI) concentration

Kinetic hydrate inhibitors (KHIs) are polymeric based chemicals that delay the nucleation and/or suppress the growth rate of gas hydrate crystals. While KHIs have been used successfully to mitigate hydrate blockage risk during oil and gas production, the mechanisms by which they function remain unclear. In this work, multiple high-pressure stirred automated lag time apparatus (HPS-ALTA) were used to investigate the impact of a KHI on the subcooling formation probability distributions of methane hydrates and the subsequent initial growth rates. Over 3000 hydrate formation events were measured around 12 MPa using seven independent HPS-ALTA cells with KHI concentrations of up to 3 wt% in water. The addition of KHI made hydrate formation much less stochastic: significant reductions occurred in both the width of the formation probability distribution for a given cell, and in the offsets between distributions measured with different cells. Average initial hydrate growth rates were reduced by approximately a factor of 5 as KHI concentration increased, even though the average driving force (subcooling) increased by a factor of up to 3. However, above a KHI concentration of 0.3 wt%, a diminishing return was observed in both the nucleation delay and growth rate suppression. A Classical Nucleation Theory (CNT) framework was applied to investigate whether polymer adsorption onto active nucleation sites could explain the observed delay in formation onset. However, the CNT kinetic parameter extracted from the measured formation probability data increased with concentration, which is opposite to the dependence predicted by the polymer-adsorption model of nucleation suppression by KHIs.

Earnings Dynamics and Intergenerational Transmission of Skill

This paper develops and estimates a two-factor model of intergenerational skill transmission when earnings inequality reflects differences in individual skills and other non-skill shocks. We consider heterogeneity in both initial skills and skill growth rates, allowing variation in skill growth to change over the lifecycle. Using administrative tax data on two linked generations of Canadians covering 37 years, we exploit covariances in log earnings (at different ages) both across and within generations to identify and estimate the intergenerational correlation structure for initial skills and skill growth rates, lifecycle skill growth profiles, and the dynamics of non-skill earnings shocks.We estimate low intergenerational elasticities (IGEs) for earnings in Canada (less than 0.2, even when based on 5- and 9-year average earnings); however, skill IGEs are typically 2-3 times larger due to considerable (and persistent) variation in earnings conditional on skills. Both earnings and skill IGEs decline substantially for more recent cohorts and are lower for children born to younger fathers.We estimate significant heterogeneity in both initial skills and skill growth rates, showing that intergenerational transmission of these factors explains up to 40% of children's skill variation. Skills become a more important determinant of earnings over the first part of workers' careers, while intergenerational transmission of skills becomes less important with age. Although inherited initial skills (compared to skill growth) are a more important determinant of children's skills throughout their lives, parents' initial skills and skill growth rates are equally important determinants of children's skills, largely because both strongly influence children's initial skills.Finally, we study intergenerational mobility for the 35 largest cities in Canada, determining the extent to which considerable differences in earnings and skill IGEs vary with the extent of local heterogeneity in parental skills vs. earnings instability. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Canada needs to rapidly escalate public health interventions for its COVID-19 mitigation strategies.

BackgroundAfter the declaration of COVID-19 pandemic on March 11th, 2020, local transmission chains starting in different countries including Canada are forcing governments to take decisions on public health interventions to mitigate the spread of the epidemic.MethodsWe conduct data-driven and model-free estimations for the growth rates of the COVID-19 epidemics in Italy and Canada, by fitting an exponential curve to the daily reported cases. We use these estimates to predict epidemic trends in Canada under different scenarios of public health interventions.ResultsIn Italy, the initial growth rate (0.22) has reduced to 0.1 two weeks after the lockdown of the country on March 8th, 2020. This corresponds to an increase of the doubling time from about 3.15 to almost 7 days. In comparison, the growth rate in Canada has increased from 0.13 between March 1st and 13th, to 0.25 between March 13th to 22nd. This current growth rate corresponds to a doubling time of 2.7 days, and therefore, unless further public health interventions are escalated in Canada, we project 15,000 cases by March 31st. However, the case number may be reduced to 4000 if escalated public health interventions could instantly reduce the growth rate to 0.1, the same level achieved in Italy.InterpretationPrompt and farsighted interventions are critical to counteract the very rapid initial growth of the COVID-19 epidemic in Canada. Mitigation plans must take into account the delayed effect of interventions by up to 2-weeks and the short doubling time of 3–4 days.