Genetic analysis of body weight and growth curve parameters in Muzaffarnagari sheep of India.

The objective of this study was to estimate genetic effects on parameters of the Brody and Richards growth curves using body weight records from birth to 12 months of age on 2287 Muzaffarnagari lamb for a period of 29 years (1976-2004). Estimated growth curve parameters were analysed using six univariate animal models, and genetic correlations among and between the parameters of each function and between parameters of the functions and observed birth and yearling weights were estimated using bivariate analyses. Significant environmental factors including birth year, sex, season, birth status and dam parity were included as fixed effects in all models. Likelihood ratio tests indicated that maternal genetic effects were significant only for birth weight (BW) and degree of maturity at birth (u0) for the Brody and Richards functions. For these traits, direct heritabilities were similar (0.21, 0.19 and 0.17, respectively), but the estimated maternal heritability for BW (0.18) was twice that of u0 for both functions. Heritabilites for yearling weight and asymptotic final body weights for the Brody and Richards functions were 0.28, 0.17 and 0.21, respectively. The remaining growth curve parameters were lowly heritable, ranging from zero for the predicted degree of maturity at the age of maximum growth rate for the Richards function to 0.08 for the maturing rate parameter of the Brody function. Genetic correlations between corresponding parameters for different growth functions exceeded 0.88. Our results showed that the Brody and Richards functions had similar genetic architecture, but the Richards function had no apparent advantages over the more easily interpreted Brody function. Failure to identify maternal genetic effects on maturing rate parameters suggested that both functions failed to identify potentially important maternal genetic effects. Therefore, there is no usefulness of estimated growth curve parameters in selection compared to the simple multi-trait genetic evaluations of individual body weights.

Combination of suitable planting density and nitrogen rate for high yield maize and their source-sink relationship in Northwest China.

BACKGROUND: Increasing crop yield per unit area by increasing planting density is essential to ensure food security. However, the optimal combination of planting density and nitrogen (N) application for high-yielding maize and its source-sink characteristics need to be more clearly understood. RESULTS: A 2-year field experiment was conducted combining three planting densities (D1: 70 000 plants ha-1 ; D2: 100 000 plants ha-1 ; D3: 130 000 plants ha-1 ) and three nitrogen rates (N1: 150 kg hm-2 ; N2: 350 kg hm-2 ; N3: 450 kg hm-2 ). The results showed that increasing planting density significantly increased leaf area index and grain yield but negatively affected ear traits. The Richards model was used to fit the dynamic changes of dry matter accumulation of maize under different treatments, and the fitting results were good. Increasing planting density increased population yield while limiting the development of individual plants, bringing the period of rapid dry matter accumulation to an early end and accelerating leaf senescence. An appropriate nitrogen rate could prolong the period of rapid accumulation of dry matter in maize, and increase the 100-kernel weight. Increasing planting density enhanced post-silking dry matter accumulation to a lesser extent, and the source-sink relationship of the maize population gradually developed from sink limitation to source limitation with increasing planting density. CONCLUSION: The decrease in yield due to the insufficient source strength to meet the sink demand at too high densities was the reason that limited further improvement of the optimal planting density. An appropriate nitrogen rate facilitated the realization of yield potential at high density. © 2023 Society of Chemical Industry.

"Not by a Decree of Fate:" Ellen Richards, Euthenics, and the Environment in the Progressive Era.

In 1904, Ellen Richards introduced "euthenics." By 1912, Lewellys Barker, director of medicine and physician-in-chief at Johns Hopkins Hospital, would tell the New York Times that the "task of eugenics" and the "task of euthenics" was the "Task for the Nation." Alongside the emergence of hereditarian eugenics, where fate was firmly rooted in heredity, this article places euthenics into the same Progressive Era demands for the scientific management over environmental issues like life and labor, health and hygiene, sewage and sanitation. I argue that euthenics not only heralded women as leaders in the quest for what Richards and eugenicists termed "racial improvement," but also aimed to make reforms through environmental and educational changes rather than hereditary interventions. Seeking to recuperate the figure of Ellen Richards in the history of science, I place Richards and her euthenics more into the debate over eugenics rather than over the emergence of home economics. Building on the work of Donald Opitz, Staffan Bergwik, and Brigette Van Tiggelen, this article shows, first, how Richards' career threads the needle between the home and the laboratory as sites of science making, not as separate spheres but as overlapping realms, and helps recover how domestic concerns shaped the focus of the life sciences. Second, this article shows how euthenics shaped eugenics by looking at the writings of American eugenicists Charles Davenport, Paul Popenoe, and David Starr Jordan. Third, the article describes how euthenics took root in new academic departments of domestic science, home economics, and departments child welfare and family life in the 1920 and 1930s, most notably the department of euthenics at the Kansas State Agricultural College from 1926 and the Institute of Euthenics at Vassar College after 1923.

A systematic review of estimation of growth curve in goats.

Growth is an economically important trait in animal production industry and is one of the subjects that can be justified mathematically. The literature recommends different non-linear model to estimate the growth of goats. The objective of this study was to systematically review the literature published on estimation of growth using non-linear models in goats. Databases such as Google Scholar, PubMed, ScienceDirect, and Web of Science were evaluated systematically using the combination of the following key terms: Non-linear growth curve models such as Brody, Richards, Gompertz, Von Bertalanffy, Logistic models. A total of 25 eligible articles were found published between 2008 and 2022 in Bangladesh, Brazil, China, Egypt, Germany, India, Indonesia, Iran, Pakistan, South Africa, Turkey, Tunisia, and Vietnam. The results showed that out of 25 articles, Gompertz growth curve model was the most used (n = 10), followed by Logistic (n = 8), then Brody growth curve model (n = 6). The findings further indicated that Janoscheck growth curve model was the least used model (n = 1) for estimation of growth in goats. One of the limitations is that some of the reviewed articles did not indicate the sex of the animals which make it difficult to draw the conclude for sexes. The systematic review concludes that Gompertz growth curve model is the most recommended for estimation of growth parameters of goats, followed by Logistic, and then Brody. Therefore, researchers should consider using these models when studying growth parameters of goats.

The accuracy of simple, feasible alternatives to polysomnography for assessing sleep in intensive care: An observational study.

BACKGROUND: Sleep disturbance is common in intensive care patients. Understanding the accuracy of simple, feasible sleep measurement techniques is essential to informing their possible role in usual clinical care. OBJECTIVE: The aim of the study was to investigate whether sleep monitoring techniques such as actigraphy (ACTG), behavioural assessments, and patient surveys are comparable with polysomnography (PSG) in accurately reporting sleep quantity and quality among conscious, intensive care patients. METHODS: An observational study was conducted in 20 patients admitted to the intensive care unit (ICU) for a minimum duration of 24 h, who underwent concurrent sleep monitoring via PSG, ACTG, nursing-based observations, and self-reported assessment using the Richards-Campbell Sleep Questionnaire. RESULTS: The reported total sleep time (TST) for the 20 participants measured by PSG was 328.2 min (±106 min) compared with ACTG (362.4 min [±62.1 min]; mean difference = 34.22 min [±129 min]). Bland-Altman analysis indicated that PSG and ACTG demonstrated clinical agreement and did not perform differently across a number of sleep variables including TST, awakening, sleep-onset latency, and sleep efficiency. Nursing observations overestimated sleep duration compared to PSG TST (mean difference = 9.95 ± 136.3 min, p > 0.05), and patient-reported TST was underestimated compared to PSG TST (mean difference = -51.81 ± 144.1 7, p > 0.05). CONCLUSIONS: Amongst conscious patients treated in the ICU, sleep characteristics measured by ACTG were similar to those measured by PSG. ACTG may provide a clinically feasible and acceptable proxy approach to sleep monitoring in conscious ICU patients.

Parameter identifiability and model selection for sigmoid population growth models.

Sigmoid growth models, such as the logistic, Gompertz and Richards' models, are widely used to study population dynamics ranging from microscopic populations of cancer cells, to continental-scale human populations. Fundamental questions about model selection and parameter estimation are critical if these models are to be used to make practical inferences. However, the question of parameter identifiability - whether a data set contains sufficient information to give unique or sufficiently precise parameter estimates - is often overlooked. We use a profile-likelihood approach to explore practical parameter identifiability using data describing the re-growth of hard coral. With this approach, we explore the relationship between parameter identifiability and model misspecification, finding that the logistic growth model does not suffer identifiability issues for the type of data we consider whereas the Gompertz and Richards' models encounter practical non-identifiability issues. This analysis of parameter identifiability and model selection is important because different growth models are in biological modelling without necessarily considering whether parameters are identifiable. Standard practices that do not consider parameter identifiability can lead to unreliable or imprecise parameter estimates and potentially misleading mechanistic interpretations. For example, using the Gompertz model, the estimate of the time scale of coral re-growth is 625 days when we estimate the initial density from the data, whereas it is 1429 days using a more standard approach where variability in the initial density is ignored. While tools developed here focus on three standard sigmoid growth models only, our theoretical developments are applicable to any sigmoid growth model and any appropriate data set. MATLAB implementations of all software are available on GitHub.

A mechanistic model for nitrogen-limited plant growth.

BACKGROUND AND AIMS: Nitrogen is often regarded as a limiting factor to plant growth in various ecosystems. Understanding how nitrogen drives plant growth has numerous theoretical and practical applications in agriculture and ecology. In 2004, Göran I. Ågren proposed a mechanistic model of plant growth from a biochemical perspective. However, neglecting respiration and assuming stable and balanced growth made the model unrealistic for plants growing in natural conditions. The aim of the present paper is to extend Ågren's model to overcome these limitations. METHODS: We improved Ågren's model by incorporating the respiratory process and replacing the stable and balanced growth assumption with a three-parameter power function to describe the relationship between nitrogen concentration (Nc) and biomass. The new model was evaluated based on published data from three studies on corn (Zea mays) growth. KEY RESULTS: Remarkably, the mechanistic growth model derived in this study is mathematically equivalent to the classical Richards model, which is the most widely used empirical growth model. The model agrees well with empirical plant growth data. CONCLUSIONS: Our model provides a mechanistic interpretation of how nitrogen drives plant growth. It is very robust in predicting growth curves and the relationship between Nc and relative growth rate.

Two-Layer numerical model of soil moisture dynamics: Model assessment and Bayesian uncertainty estimation.

A two-layer model based on the integrated form of Richards' equation (RE) was recently developed to simulate the soil water movement in the roots layer and the vadose zone with a relatively shallow and dynamic water table. The model simulates thickness-averaged volumetric water content and matric suction as opposed to point values and was numerically verified for three soil textures using HYDRUS as a benchmark. However, the strengths and limitations of the two-layer model and its performance in stratified soils and under actual field conditions have not been tested. This study further examined the two-layer model using two numerical verification experiments and, most importantly, tested its performance at site-level under actual, highly variable hydroclimate conditions. Moreover, model parameters were estimated and uncertainty and sources of errors were quantified using a Bayesian framework. First, the two-layer model was evaluated for 231 soil textures under varying soil layer thicknesses with a uniform soil profile. Second, the two-layer model was assessed for stratified conditions where the top and bottom soil layers have contrasting hydraulic conductivities. The model was evaluated by comparing soil moisture and flux estimates to those from the HYDRUS model. Last, a case study of model application using data from a Soil Climate Analysis Network (SCAN) site was presented. Bayesian Monte Carlo (BMC) method was implemented for model calibration and quantifying sources of uncertainty under real hydroclimate and soil conditions. For a homogeneous soil profile, the two-layer model generally had excellent performance in estimating volumetric water content and fluxes, while the model performance slightly declined with increasing layer thickness and coarser textured soils. The model configurations regarding layer thicknesses and soil textures that generate accurate soil moisture and flux estimations were further suggested. With the two layers of contrasting permeability, model-simulated soil moisture contents and fluxes agreed well with those computed by HYDRUS, indicating that the two-layer model accurately handles the water flow dynamics around the layer interface. In the field application, given the highly variable hydroclimate conditions, the two-layer model combined with the BMC method showed good agreement with the observed average soil moisture of the root zone and the vadose zone below (RMSE <0.021 during calibration and <0.023 during validation periods). The contribution of parametric uncertainty to the total model uncertainty was too small compared to other sources. The numerical tests and the site level application showed that the two-layer model can reliably simulate thickness-averaged soil moisture and estimate fluxes in the vadose zone under various soil and hydroclimate conditions. Results also indicated that the BMC method could be a robust framework for vadose zone hydraulic parameters identification and model uncertainty estimation.

Absolute Quantitation of Serum Antibody Reactivity Using the Richards Growth Model for Antigen Microspot Titration.

In spite of its pivotal role in the characterization of humoral immunity, there is no accepted method for the absolute quantitation of antigen-specific serum antibodies. We devised a novel method to quantify polyclonal antibody reactivity, which exploits protein microspot assays and employs a novel analytical approach. Microarrays with a density series of disease-specific antigens were treated with different serum dilutions and developed for IgG and IgA binding. By fitting the binding data of both dilution series to a product of two generalized logistic functions, we obtained estimates of antibody reactivity of two immunoglobulin classes simultaneously. These estimates are the antigen concentrations required for reaching the inflection point of thermodynamic activity coefficient of antibodies and the limiting activity coefficient of antigen. By providing universal chemical units, this approach may improve the standardization of serological testing, the quality control of antibodies and the quantitative mapping of the antibody-antigen interaction space.

Psychometric properties of a Thai version of the Richards-Campbell sleep questionnaire.

BACKGROUND: In critically ill patients, a poor sleep quality can escalate mortality and the length of hospital stays. Albeit being the gold standard for sleep assessment, polysomnography (PSG) is expensive and complicated. The Richards-Campbell sleep questionnaire (RCSQ) is another tool with proof of good correlation with PSG. RCSQ was translated into many languages. However, the Thai version (T-RCSQ) has not been developed. AIMS AND OBJECTIVES: Our study aimed to translate the original RCSQ into Thai, to test the content validity and reliability, and to introduce the questionnaire into clinical practice at the surgical intensive care unit (SICU). DESIGN: Prospective cross-sectional study METHODS: This study enrolled 92 patients from the SICU between August 2019 and January 2020. The content validity of T-RCSQ was determined by the index of item-objective congruence (IOC). The reliability was tested by test-retest reliability at 7 am and 9 am after intensive care unit (ICU) admission. The internal consistency was expressed by Cronbach's alpha. Patients' demography was reported as percentage, mean and standard deviation, and median and interquartile range. RESULTS: The content validity and test-retest reliability of the T-RCSQ were 0.8 and 0.97, respectively. The internal consistency was 0.964. Most patients were female with American Society of Anesthesiologist physical status III. The mean RCSQ scores at 7 am and 9 am were 5.82 ± 2.15 cm and 5.61 ± 2.18 cm, respectively. CONCLUSIONS: The T-RCSQ is reliable and could be used as an alternative to PSG for sleep assessment of ICU patients. Further research is required to validate the T-RCSQ against PSG and to assess its impact on improving sleep quality and patients' clinical outcomes. RELEVANCE TO CLINICAL PRACTICE: T-RCSQ is a useful tool for sleep assessment in ICU. According to cost-effectiveness, convenience, and good reliability, it could be applied to determine proper sleep to minimize patient morbidity and mortality.

Investigating the construct and concurrent validity of the Richards-Campbell Sleep Questionnaire with intensive care unit patients and home sleepers.

BACKGROUND: Sleep is vital to our wellbeing. Critically ill patients are vulnerable with effects of sleep deprivation including weakened immune function, decreased glucose tolerance, and increased sympathetic activity. Intensive care unit (ICU) patients' sleep evaluation is difficult and often not reliable. The most commonly used instrument for assessing ICU patients' perspective of their sleep, Richards-Campbell Sleep Questionnaire (RCSQ), has not been reported to have undergone known-group construct validity testing or concurrent validity testing with the criterion measure of feeling refreshed. OBJECTIVES: The aim of the study was to explore the construct validity of the RCSQ with known-groups technique and concurrent validity with the criterion measure of feeling refreshed on awakening. METHODS: A cross-sectional descriptive survey study using the RCSQ was conducted on people sleeping at home (n = 114) over seven nights. The results were compared with the RCSQ sleep scores of nonintubated alert oriented adult ICU patients (n = 114). Home sleepers were also asked to rate how refreshed they felt on awakening. The study was executed and reported in accordance with the STROBE checklist for observational studies. FINDINGS: RCSQ construct validity was supported because home sleepers' and ICU sleepers' sleep evaluations differed significantly. Home sleepers rated their sleep significantly better than ICU patients in all five sleep domains of the RCSQ. Concurrent validity was supported because the item "feeling refreshed on awakening" correlated strongly with all sleep domains. CONCLUSIONS: Sleep quality may be accurately measured using the RCSQ in alert people both in the ICU and at home. This study has added to the validity discussion around the RCSQ. The RCSQ can be used for sleep evaluation in ICUs to promote wellbeing and recovery.

Nowcasting COVID-19 incidence indicators during the Italian first outbreak.

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.

Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach.

BACKGROUND: The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. METHODS: In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. RESULTS: We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551-26,702 cases in 5 days and an additional 47,449-57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145-437 COVID-19 deaths in 5 days and an additional 243-947 deaths in 10 days. CONCLUSIONS: By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.

Ensemble bootstrap methodology for forecasting dynamic growth processes using differential equations: application to epidemic outbreaks.

BACKGROUND: Ensemble modeling aims to boost the forecasting performance by systematically integrating the predictive accuracy across individual models. Here we introduce a simple-yet-powerful ensemble methodology for forecasting the trajectory of dynamic growth processes that are defined by a system of non-linear differential equations with applications to infectious disease spread. METHODS: We propose and assess the performance of two ensemble modeling schemes with different parametric bootstrapping procedures for trajectory forecasting and uncertainty quantification. Specifically, we conduct sequential probabilistic forecasts to evaluate their forecasting performance using simple dynamical growth models with good track records including the Richards model, the generalized-logistic growth model, and the Gompertz model. We first test and verify the functionality of the method using simulated data from phenomenological models and a mechanistic transmission model. Next, the performance of the method is demonstrated using a diversity of epidemic datasets including scenario outbreak data of the Ebola Forecasting Challenge and real-world epidemic data outbreaks of including influenza, plague, Zika, and COVID-19. RESULTS: We found that the ensemble method that randomly selects a model from the set of individual models for each time point of the trajectory of the epidemic frequently outcompeted the individual models as well as an alternative ensemble method based on the weighted combination of the individual models and yields broader and more realistic uncertainty bounds for the trajectory envelope, achieving not only better coverage rate of the 95% prediction interval but also improved mean interval scores across a diversity of epidemic datasets. CONCLUSION: Our new methodology for ensemble forecasting outcompete component models and an alternative ensemble model that differ in how the variance is evaluated for the generation of the prediction intervals of the forecasts.

Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units.

This paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital's data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital's control centre and is used in several Dutch hospitals during the second COVID-19 peak.

A novel grey model based on traditional Richards model and its application in COVID-19.

In 2020, a new type of coronavirus is in the global pandemic. Now, the number of infected patients is increasing. The trend of the epidemic has attracted global attention. Based on the traditional Richards model and the differential information principle in grey prediction model, this paper uses the modified grey action quantity to propose a new grey prediction model for infectious diseases. This model weakens the dependence of the Richards model on single-peak and saturated S-shaped data, making Richards model more applicable, and uses genetic algorithm to optimize the nonlinear terms and the background value. To illustrate the effectiveness of the model, groups of slowly growing small-sample and large-sample data are selected for simulation experiments. Results of eight evaluation indexes show that the new model is better than the traditional GM(1,1) and grey Richards model. Finally, this model is applied to China, Italy, Britain and Russia. The results show that the new model is better than the other 7 models. Therefore, this model can effectively predict the number of daily new confirmed cases of COVID-19, and provide important prediction information for the formulation of epidemic prevention policies.

A two-layer numerical model of soil moisture dynamics: Model development.

Simulating water moisture flow in variably saturated soils with a relatively shallow water table is challenging due to the high nonlinear behavior of Richards' equation (RE). A two-layer approximation of RE was derived in this paper, which describes vertically-averaged soil moisture content and flow dynamics in the root zone and the unsaturated soil below. To this end, the partial differential equation (PDE) describing RE was converted into two-coupled ordinary differential equations (ODEs) describing dynamic vertically-averaged soil moisture variations in the two soil zones subject to a deep or shallow water table in addition to variable soil moisture flux and pressure conditions at the surface. The coupled ODEs were solved numerically using the iterative Huen's method for a variety of flux and pressure-controlled top and bottom boundary conditions (BCs). The numerical model was evaluated for three typical soil textures with free-drainage and mixed flux-pressure head at the bottom boundary under various atmospheric conditions. The results of soil water contents and fluxes were validated using HYDRUS-1D as a benchmark. Simulated values showed that the new model is numerically stable and generally accurate in simulating vertically-averaged soil moisture in the two layers under various flux and prescribed pressure BCs. A hypothetical simulation scenario involving desaturation of initially saturated soil profile caused by exponentially declining water table demonstrated the robustness of the numerical model in tracking vertically-averaged moisture contents in the roots layer and the lower vadose soil as the water table continued to fall. The two-layer model can be used by researchers to simulate variably saturated soils in wetlands and by water resources planners for efficient coupling of land-surface systems to groundwater and management of conjunctive use of surface and groundwater.

Spin-Orbital Conversion of a Strongly Focused Light Wave with High-Order Cylindrical-Circular Polarization.

We discuss interesting effects that occur when strongly focusing light with mth-order cylindrical-circular polarization. This type of hybrid polarization combines properties of the mth-order cylindrical polarization and circular polarization. Reluing on the Richards-Wolf formalism, we deduce analytical expressions that describe E- and H-vector components, intensity patterns, and projections of the Poynting vector and spin angular momentum (SAM) vector at the strong focus. The intensity of light in the strong focus is theoretically and numerically shown to have an even number of local maxima located along a closed contour centered at an on-axis point of zero intensity. We show that light generates 4m vortices of a transverse energy flow, with their centers located between the local intensity maxima. The transverse energy flow is also shown to change its handedness an even number of times proportional to the order of the optical vortex via a full circle around the optical axis. It is interesting that the longitudinal SAM projection changes its sign at the focus 4m times. The longitudinal SAM component is found to be positive, and the polarization vector is shown to rotate anticlockwise in the focal spot regions where the transverse energy flow rotates anticlockwise, and vice versa-the longitudinal SAM component is negative and the polarization vector rotates clockwise in the focal spot regions where the transverse energy flow rotates clockwise. This spatial separation at the focus of left and right circularly polarized light is a manifestation of the optical spin Hall effect. The results obtained in terms of controlling the intensity maxima allow the transverse mode analysis of laser beams in sensorial applications. For a demonstration of the proposed application, the metalens is calculated, which can be a prototype for an optical microsensor based on sharp focusing for measuring roughness.

Minimal Focal Spot Size Measured Based on Intensity and Power Flow.

It is shown, theoretically and numerically, that the distributions of the longitudinal energy flow for tightly focused light with circular and linear polarization are the same, and that the spot has circular symmetry. It is also shown that the longitudinal energy flows are equal for optical vortices with unit topological charge and with radial or azimuthal polarization. The focal spot has a minimum diameter (all other characteristics being equal), which is measured based on the intensity of an optical vortex with azimuthal polarization. The diameter of the focal spot calculated from the energy flow for light with circular or linear polarization is slightly larger (by a fraction of a percentage). The magnitude of the diameter based on the intensity plays a role in the interaction of light with matter, and the magnitude of the diameter based on the energy flux affects the resolution in optical microscopy which is crucial in sensorial applications.

Strain Variability of Listeria monocytogenes under NaCl Stress Elucidated by a High-Throughput Microbial Growth Data Assembly and Analysis Protocol.

Listeria monocytogenes causes the severe foodborne illness listeriosis and survives in food-associated environments due to its high stress tolerance. A data assembly and analysis protocol for microbial growth experiments was compiled to elucidate the strain variability of L. monocytogenes stress tolerance. The protocol includes measurement of growth ability under stress (step 1), selection of a suitable method for growth parameter calculation (step 2), comparison of growth patterns between strains (step 3), and biological interpretation of the discovered differences (step 4). In step 1, L. monocytogenes strains (n = 388) of various serovars and origins grown on media with 9.0% NaCl were measured using a Bioscreen C microbiology reader. Technical variability of the growth measurements was assessed and eliminated. In step 2, the growth parameters determined by Gompertz, modified-Gompertz, logistic, and Richards models and model-free splines were compared, illustrating differences in the suitability of these methods to describe the experimental data. In step 3, hierarchical clustering was used to describe the NaCl tolerance of L. monocytogenes measured by strain-specific variation in growth ability; tolerant strains had higher growth rates and maximum optical densities and shorter lag phases than susceptible strains. The spline parameter area under the curve best classified "poor," "average," and "good" growers. In step 4, the tested L. monocytogenes lineage I strains (serovars 4b and 1/2b) proved to be significantly more tolerant toward 9.0% NaCl than lineage II strains (serovars 1/2a, 1/2c, and 3a). Our protocol provides systematic tools to gain comparable data for investigating strain-specific variation of bacterial growth under stress.IMPORTANCE The pathogen Listeria monocytogenes causes the foodborne disease listeriosis, which can be fatal in immunocompromised individuals. L. monocytogenes tolerates several environmental stressors and can persist in food-processing environments and grow in foodstuffs despite traditional control measures such as high salt content. Nonetheless, L. monocytogenes strains differ in their ability to withstand stressors. Elucidating the intraspecies strain variability of L. monocytogenes stress tolerance is crucial for the identification of particularly tolerant strains. To enhance reliable identification of variability in bacterial stress tolerance phenotypes, we compiled a large-scale protocol for the entire data assembly and analysis of microbial growth experiments, providing a systematic approach and checklist for experiments on strain-specific growth ability. Our study illustrated the diversity and strain-specific variation of L. monocytogenes stress tolerance with an unprecedented scope and discovered biologically relevant serovar- and lineage-dependent phenotypes of NaCl tolerance.