Forecasting COVID-19 infection trends in the EU-27 countries, the UK and Switzerland due to SARS-CoV-2 Variant of Concern Omicron

Background: On November 26, 2021, WHO designated the variant B.1.1.529 as a new SARS-CoV-2 variant of concern (VoC), named Omicron, originally identified in South Africa. Several mutations in Omicron indicate that it may have an impact on how it spreads, resistance to vaccination, or the severity of illness it causes. Methods: We used our previous modelling algorithms to forecast the spread of Omicron aggregated in the EU-27 countries, the United Kingdom and Switzerland, and report trends in daily cases with a 7-day moving average. We followed EQUATOR TRIPOD guidance for multivariable prediction models. Modelling included a third-degree polynomial curve in existing epidemiological trends on the spread of Omicron in South Africa, a five-parameter logistic (5PL) asymmetrical sigmoidal curve following a parametric growth in Europe, and a new Gaussian curve to estimate a downward trend after a peak. Results: Up to January 15, 2022, we estimated a background rate projection in EU-27 countries, the UK and Switzerland of about 145,000 COVID-19 daily cases without Omicron, which increases up to 440,000 COVID-19 daily cases in the worst scenario of Omicron spread, and 375,000 in the best scenario. Therefore, Omicron might represent a relative increase from the background daily rates of COVID-19 infection in Europe of 1.03-fold or 2.03-fold, that is up to a 200% increase. Conclusion: This warning pandemic surge due to Omicron is calling for further reinforcing of COVID-19 universal hygiene interventions (indoor ventilation, social distance, and face masks), and anticipating the need of new lockdowns in Europe.

Relating genome completeness to functional predictions

Genome and transcriptome assemblies vary in their quality in terms of the connectedness of the assemblies and the amount of biological information captured. Interpreting de novo assemblies from new, poorly characterized, organisms in the context of complex traits can be challenging because, in the absence of a reference, it is difficult to know how much information is enough to claim the presence or absence of a trait. This study uses randomly downsampled proteome files to compare a genome completeness metric, BUSCO, to functional predictions of the complex trait of phagocytosis in known phagocytotic organisms broadly across the eukaryotic tree of life. We find that as additional proteins are added, BUSCO scores increase incrementally, while the phagocytosis prediction follows a sigmoidal curve. Generalizing our findings, we suggest a threshold of the number of BUSCOs detected above which one would expect an accurate prediction, positive or negative, of the complex trait of phagocytosis. While these findings are specific to a single trait, the methods can be extended to consider additional functional traits and predictive frameworks.

Baroreflex Curve Fitting Using a WYSIWYG Boltzmann Sigmoidal Equation

Arterial baroreflex assessment using vasoactive substances enables investigators to collect data pairs over a wide range of blood pressures and reflex reactions. These data pairs relate intervals between heartbeats or sympathetic neural activity to blood pressure values. In an X-Y plot the data points scatter around a sigmoidal curve. After fitting the parameters of a sigmoidal function to the data, the graph’s characteristics represent a rather comprehensive quantitative reflex description. Variants of the 4-parameter Boltzmann sigmoidal equation are widely used for curve fitting. Unfortunately, their ‘slope parameters’ do not correspond to the graph’s actual slope which complicates the analysis and bears the risk of misreporting. We propose a modified Boltzmann sigmoidal function with preserved goodness of fit whose parameters are one-to-one equivalent to the sigmoidal curve’s characteristics.

Moore’s Law revisited through Intel chip density

Gordon Moore famously observed that the number of transistors in state-of-the-art integrated circuits (units per chip) increases exponentially, doubling every 12–24 months. Analysts have debated whether simple exponential growth describes the dynamics of computer processor evolution. We note that the increase encompasses two related phenomena, integration of larger numbers of transistors and transistor miniaturization. Growth in the number of transistors per unit area, or chip density, allows examination of the evolution with a single measure. Density of Intel processors between 1959 and 2013 are consistent with a biphasic sigmoidal curve with characteristic times of 9.5 years. During each stage, transistor density increased at least tenfold within approximately six years, followed by at least three years with negligible growth rates. The six waves of transistor density increase account for and give insight into the underlying processes driving advances in processor manufacturing and point to future limits that might be overcome.

Testing for a Sweet Spot in Randomized Trials

Introduction Randomized trials recruit diverse patients, including some individuals who may be unresponsive to the treatment. Here we follow up on prior conceptual advances and introduce a specific method that does not rely on stratification analysis and that tests whether patients in the intermediate range of disease severity experience more relative benefit than patients at the extremes of disease severity (sweet spot). Methods We contrast linear models to sigmoidal models when describing associations between disease severity and accumulating treatment benefit. The Gompertz curve is highlighted as a specific sigmoidal curve along with the Akaike information criterion (AIC) as a measure of goodness of fit. This approach is then applied to a matched analysis of a published landmark randomized trial evaluating whether implantable defibrillators reduce overall mortality in cardiac patients ( n = 2,521). Results The linear model suggested a significant survival advantage across the spectrum of increasing disease severity (β = 0.0847, P < 0.001, AIC = 2,491). Similarly, the sigmoidal model suggested a significant survival advantage across the spectrum of disease severity (α = 93, β = 4.939, γ = 0.00316, P < 0.001 for all, AIC = 1,660). The discrepancy between the 2 models indicated worse goodness of fit with a linear model compared to a sigmoidal model (AIC: 2,491 v. 1,660, P < 0.001), thereby suggesting a sweet spot in the midrange of disease severity. Model cross-validation using computational statistics also confirmed the superior goodness of fit of the sigmoidal curve with a concentration of survival benefits for patients in the midrange of disease severity. Conclusion Systematic methods are available beyond simple stratification for identifying a sweet spot according to disease severity. The approach can assess whether some patients experience more relative benefit than other patients in a randomized trial. [Box: see text]

Incidence, Spatial Pattern and Temporal Progress of Fusarium Wilt of Bananas

The effective management of Fusarium wilt of bananas (FWB) depends on the knowledge of the disease dynamics in time and space. The objectives of this work were: to estimate disease intensity and impact, and to investigate the spatial and temporal dynamics of FWB. Fields planted with Silk (n = 10), Pome (n = 17), or Cavendish (n = 3) banana subgroups were surveyed in Brazil, totaling 95 ha. In each field, all plants were visually assessed, and diseased plants were georeferenced. The incidence of FWB and the impact of the disease on the yield on a regional scale were estimated. Spatial patterns were analyzed using quadrat- and distance-based methods. FWB incidence ranged from 0.09% to 41.42%, being higher in Silk fields (median = 14.26%). Impacts of epidemics on yield ranged from 18.4 to 8192.5 kg ha−1 year−1, with an average of 1856.7 kg ha−1 year−1. The higher economic impact of the disease was observed on Silk cultivar with an average loss of USD 1974.2 ha−1 year−1. Overall, estimated losses increased on average by USD 109.8 ha−1 year−1 at each 1% of incidence. Aggregation of FWB was detected by all analytical methods in 13 fields (1 of Cavendish, 11 of Pome, and 1 of Silk). In the other 17 fields, at least one analytical method did not reject the null hypothesis of randomness. One field (5 ha), composed of six plots, was selected for spatial and temporal studies during two years with bi-monthly assessments. A sigmoidal curve represented the FWB progress and the Gompertz model best-fitted disease progress. The level of aggregation varied over time, and evidence of secondary infection to neighboring and distant plants was detected. FWB is a widespread problem in Brazil and yield losses can be of high magnitude. Epidemiology-based management strategies can now be better established.

Incidence, Spatial Pattern and Temporal Progress of Fusarium Wilt of Bananas

The effective management of Fusarium wilt of bananas (FW) depends on the knowledge of the disease dynamics in time and space. The objectives of this work were: To estimate disease intensity and impact, and to investigate the spatial and temporal dynamic of FW. Fields planted with Silk (n = 10), Pome (n = 17) or Cavendish (n = 3) banana subgroups were surveyed in Brazil, totaling 95 ha. In each field, all plants were visually assessed and diseased plants were georeferenced. The incidence of FW and the impact of the disease on yield on a regional scale were estimated. Spatial patterns were analyzed using quadrat- and distance-based methods. FW incidence ranged from 0.09 to 41.42%, being higher in Silk fields (median = 14.26%). Impacts of epidemics on yield ranged from 18.4 to 8,192.5 kg.ha-1.year-1, with a median of 935.2 kg.ha-1.year-1. The higher economic impact of the disease was observed on Silk cultivar with a median loss of US$ 910.5 ha-1.year-1. Overall, estimated losses increased on average by US$ 109.8 ha-1.year-1 at each 1% of incidence. Aggregation of FW was detected by all analytical methods in 13 fields (1 of Cavendish, 11 of Pome and 1 of Silk). In the other 17 fields, at least one analytical method did not reject the null hypothesis of randomness. One field (5 ha), composed of six plots, was selected for spatial and temporal studies during two years with bi-monthly assessments. A sigmoidal curve represented the FW progress and the Gompertz model best fitted disease progress. The level of aggregation varied over time, and evidence of secondary infection to neighboring and distant plants were detected. FW is a widespread problem in Brazil and yield losses can be of high magnitude. Epidemiology-based management strategies can now be better established.

Simplified sigmoidal curve fitting for a 6 MV FFF photon beam of the Halcyon to determine the field size for beam commissioning and quality assurance

Background An O-ring gantry-type linear accelerator (LINAC) with a 6-MV flattening filter-free (FFF) photon beam, Halcyon, includes a reference beam that contains representative information such as the percent depth dose, profile and output factor for commissioning and quality assurance. However, because it does not provide information about the field size, we proposed a method to determine all field sizes according to all depths for radiation therapy using simplified sigmoidal curve fitting (SCF). Methods After mathematical definition of the SCF using four coefficients, the defined curves were fitted to both the reference data (RD) and the measured data (MD). For good agreement between the fitting curve and the profiles in each data set, the field sizes were determined by identifying the maximum point along the third derivative of the fitting curve. The curve fitting included the field sizes for beam profiles of 2 × 2, 4 × 4, 6 × 6, 8 × 8, 10 × 10, 20 × 20 and 28 × 28 cm2 as a function of depth (at 1.3, 5, 10 and 20 cm). The field size results from the RD were compared with the results from the MD using the same condition. Results All fitting curves show goodness of fit, R2, values that are greater than 0.99. The differences in field size between the RD and the MD were within the range of 0 to 0.2 cm. The smallest difference in the field sizes at a depth of 10 cm, which is a surface-to-axis distance, was reported. Conclusion Application of the SCF method has been proven to accurately capture the field size of the preconfigured RD and the measured FFF photon beam data for the Halcyon system. The current work can be useful for beam commissioning as a countercheck methodology to determine the field size from RD in the treatment planning system of a newly installed Halcyon system and for routine quality assurance to ascertain the correctness of field sizes for clinical use of the Halcyon system.

Simplified sigmoidal curve fitting for a 6 MV FFF photon beam of Halcyon to determine the field size for beam commissioning and quality assurance

Background : An O-ring gantry-type linear accelerator (Linac) with a 6 MV flattening filter-free (FFF) photon beam, Halcyon, includes a reference beam containing the representative information regarding the percent depth dose, profile, output factor, etc. for commissioning and quality assurance. However, because it does not provide the information of the field size, we proposed a method to determine all field sizes according to all depths for radiation therapy using simplified sigmoidal curve fitting (SCF). Methods : After a mathematical definition of the SCF using four coefficients, the defined curves were fitted to both the reference and measured data. For a high agreement between the fitting curve and the profiles in each data set, the field sizes were determined by identifying the maximum point along the third derivative of the fitting curve. The curve fitting included the field sizes for beam profiles of 2 × 2, 4 × 4, 6 × 6, 8 × 8, 10 × 10, 20 × 20 and 28 × 28 cm 2 as a function of depth (at 1.3, 5, 10, 20 cm). The results of the field size from the reference data were compared with the results in the measured data using the same condition. Results : All fitting curves show values of goodness of fit, R 2 , better than 0.99. The differences in the field size between the reference data and the measured data were within the range of 0 to 0.2 cm. The lowest difference in the field sizes at a depth of 10 cm, which is a surface-to-axis distance, was reported. Conclusion : The application of the SCF has been proven to accurately obtain the field size of the preconfigured reference and of the measured FFF photon beam data for the Halcyon system. The current work can be useful for beam commissioning as a counte-rcheck methodology to determine the field size from the reference data in the treatment planning system of a newly installed Halcyon system and for the routine quality assurance to ascertain the correctness of field sizes clinically used with the Halcyon system.

The Relationship between Soil Moisture and Soil Water Repellency Persistence in Hydrophobic Soils

In this work, we modelled the response of soil water repellency (SWR) persistence to the decrease in moisture in drying soils, and we explored the implication of soil particle size distribution and specific surface area on the SWR severity and persistence. A new equation for the relationship between SWR persistence and soil moisture (θ) is described in this paper. The persistence of SWR was measured on ten different hydrophobic soils using water drop penetration time (WDPT) at decreasing levels of gravimetric water content. The actual repellency persistence showed a sigmoidal response to soil moisture decrease, where Ra(θ)=Rp/1+eδ(θ−θc). The suggested equation enables one to model the actual SWR persistence (Ra) using θ, the potential repellency (Rp) and two characteristic parameters related to the shape of the response curve. The two parameters are the critical soil moisture θc, where the Ra increase rate reaches its maximum, and the parameter δ affecting the steepness of the curve at the inflexion point of the sigmoidal curve. Data shows that both soil carbon and texture are controlling the potential SWR in New Zealand pastures.