Function Of Population Density Research Articles

Bacterial-fungal crosstalk is defined by a fungal lactone mycotoxin and its degradation by a bacterial lactonase.

Chemical signaling in the microbial world facilitates the regulation of gene expression as a function of cell population density. This is especially true for the Gram-negative bacterial signal N-acyl homoserine lactone (AHL). Lactonases that deactivate AHLs have attracted a lot of attention because of their antibacterial potential. However, the involvement of these enzymes in inhibiting fungal pathogens and the potential role of these enzymes in bacterial-fungal interactions are unknown. Here, we find that a bacterial enzyme involved in the degradation of AHLs is also induced by and degrades the fungal lactone mycotoxin, patulin. This work supports the potential use of bacterial enzymes and/or the producing bacteria in controlling the post-harvest fruit disease caused by the patulin-producing fungus Penicillium expansum.

Undernourishment on Longevity in West Africa: A Group Outcome

The length of time a person lives from the date of birth is an essential dimension of a country’s level of development. Several factors have been identified as responsible for longevity. This study investigated the effect of undernourishment prevalence and other factors on longevity in West Africa from 2000-2018. Using panel data, the random coefficient model was employed to examine the effect of a novel variable, such as the prevalence of undernourishment and others (government expenditure on health, population density, unemployment and out-of-pocket expenditure) on longevity. Except for a few, the findings revealed that longevity is a decreasing function of the prevalence of undernourishment in West Africa. In contrast, it was revealed that longevity is an increasing function of population density in all West African countries. The effect of government spending and unemployment on health contradicts theory in some countries, while they were in tandem with theory in other countries. Therefore, the study recommended, among others, the introduction of dynamic agricultural policies to increase income and ensure food security in West Africa.

Automated feature analysis as a tool for non-metallic inclusion analysis in solidified metals

In-depth analysis of the non-metallic inclusion population in solidified metals is essential considering the high cleanliness requirements for metallic components. For this purpose, automated feature analysis (AFA) is a powerful inclusion analysis tool, providing statistical supported information about quantity, elemental composition, morphology and size of inclusions. However, thorough data evaluation is imperative to ensure accurate and reliable results. The paper introduces the key principles of evaluating AFA data, emphasizing its capabilities and constraints by utilizing four large datasets obtained from solidified steel blocks, each containing a minimum of 4000 features. Additionally, methodologies for identifying characteristic inclusion species within a dataset, constructing suitable rulefiles for inclusion classification through statistical analyzes, and interpreting the resulting outcomes are presented. The size distribution of inclusions is analyzed using population density functions, focusing on the absolute inclusion count within specific size ranges. Cluster analysis is presented considering the inclusions‘ positional parameters.

Spatial Agglomeration and Diffusion of Population Based on a Regional Density Function Approach: A Case Study of Shandong Province in China

Population density functions have long been used to describe the spatial structure of regional population distributions. Several studies have been conducted to examine the population distribution in Shandong Province, China, but few have applied regional density functions to the analysis. Therefore, based on the 2000, 2010, and 2020 population censuses, this study used monocentric and polycentric regional density functions to study the characteristics of population agglomeration and diffusion in Shandong. This is followed by an in-depth discussion based on population growth rate data and hot- and cold-spot analyses. The results showed that the Shandong Province population was spatially unevenly distributed. Population growth rates were higher in urban centers and counties, with more significant changes in population size in the eastern coastal areas than in the inland areas. As verified in this study, the logarithmic form of the single-center regional density function R2 was greater than 0.8, which was in line with the population spatial structure of Shandong Province. During the study period, the estimated population density of the regional center and the absolute value of the regional population density gradient both increased, indicating a clear and increasing trend of centripetal agglomeration of regional centers over the study period. Overall, the R2 value of the multicenter region density function was higher than that of the single-center region density function. The polycentric regional density function showed that the population density gradient of some centers had a downward trend, which reflected the spatial development trend of outward diffusion in these centers. Meanwhile, the variation in the estimated population density and the population density gradient exhibited differences in the central population distribution patterns at different levels.

The asymmetric particle population density method for simulation of coupled noisy oscillators

Many biological phenomena can be modeled by the collective activity of a population of individual units. A common strategy for simulating such a system, the population density approach, is to take the macroscopic limit and track a population density function. Here, we develop the asymmetric particle population density (APPD) method that efficiently and accurately simulates populations with complex behaviors that are infeasible for previous population density-based methods. The APPD method is well-suited for a parallel implementation. Our method can accurately reproduce complex macroscopic behaviors such as inhibitory coupling-induced clustering and noise-induced firing while being faster than the direct simulation. We compare the method's performance against direct Monte-Carlo simulation and verify its accuracy by applying it to the well-studied Hodgkin-Huxley model with a range of challenging scenarios.

Traits shaping urban tolerance in birds differ around the world

As human density increases, biodiversity must increasingly co-exist with urbanization or face local extinction. Tolerance of urban areas has been linked to numerous functional traits, yet few globally consistent patterns have emerged to explain variation in urban tolerance, which stymies attempts at a generalizable predictive framework. Here, we calculate an Urban Association Index (UAI) for 3,768 bird species in 137 cities across all permanently inhabited continents. We then assess how this UAI varies as a function of ten species-specific traits and further test whether the strength of trait relationships vary as a function of three city-specific variables. Of the ten species traits, nine were significantly associated with urban tolerance. Urban-associated species tend to be smaller, less territorial, have greater dispersal ability, broader dietary and habitat niches, larger clutch sizes, greater longevity, and lower elevational limits. Only bill shape showed no global association with urban tolerance. Additionally, the strength of several trait relationships varied across cities as a function of latitude and/or human population density. For example, the associations of body mass and diet breadth were more pronounced at higher latitudes, while the associations of territoriality and longevity were reduced in cities with higher population density. Thus, the importance of trait filters in birds varies predictably across cities, indicating biogeographic variation in selection for urban tolerance that could explain prior challenges in the search for global patterns. A globally informed framework that predicts urban tolerance will be integral to conservation as increasing proportions of the world's biodiversity are impacted by urbanization.

Large-scale identification of known and novel RRNPP quorum sensing systems by RRNPP_detector captures novel features of bacterial, plasmidic and viral co-evolution.

Gram-positive Firmicutes bacteria and their mobile genetic elements (plasmids, bacteriophages) encode peptide-based quorum sensing systems (QSSs) that orchestrate behavioral transitions as a function of population densities. In their simplest form, termed "RRNPP", these QSSs are composed of two adjacent genes: a communication propeptide and its cognate intracellular receptor. RRNPP QSSs notably regulate social/competitive behaviors such as virulence or biofilm formation in bacteria, conjugation in plasmids or lysogeny in temperate bacteriophages. However, the genetic diversity and the prevalence of these communication systems, together with the breadth of behaviors they control, remain largely under-appreciated. To better assess the impact of density-dependency on microbial community dynamics and evolution, we developed the RRNPP_detector software, which predicts known and novel RRNPP QSSs in chromosomes, plasmids and bacteriophages of Firmicutes. Applying RRNPP_detector against available complete genomes of viruses and Firmicutes, we identified a rich repertoire of RRNPP QSSs from 11 already-known subfamilies and 21 novel high-confidence candidate subfamilies distributed across a vast diversity of taxa. The analysis of high-confidence RRNPP subfamilies notably revealed 14 subfamilies shared between chromosomes/plasmids/phages, 181 plasmids and 82 phages encoding multiple communication systems, phage-encoded QSSs predicted to dynamically modulate bacterial behaviors, and 196 candidate biosynthetic gene clusters under density-dependent regulation. Overall, our work enhances the field of quorum sensing research and reveals novel insights into the co-evolution of gram-positive bacteria and their mobile genetic elements.

Comparison of the Dynamic and Thermal Behavior of Different Ideal Flow Crystallizers

In this simulation study, we compare the dynamics and thermal behavior of different ideal flow crystallizers. The first step in creating mathematical models for the crystallizers was the implementation of the population balance equation. The population balance equation was completed with mass balance equations for the solute and the solvent as well as in the case of non-isothermal crystallizers with an energy balance equation. The solution to the population balance equation, which is a partial differential equation, can only be performed numerically. Using the method of moments, which calculates the moments of the population density function, gives a mathematically simpler model for simulating and analyzing the crystallizers. All crystallizers studied are considered mixed suspension and mixed product crystallizers. In this simulation study, the investigated crystallizers are the batch mixed suspension and mixed product isothermal crystallizer, the batch mixed suspension and mixed product non-isothermal crystallizer, and the continuous mixed suspension and mixed product removal (CMSMPR) non-isothermal crystallizer equipped with a cooling jacket. We consider citric acid as the solid material to be crystallized, and a water–glycol system is used as a cooling medium. Considering the nucleation kinetics, we applied both primary and secondary nucleation. In the case of a crystal growth kinetic, we assumed a size-independent growth rate. The highest expected value and the variance of the crystal product occur in the isotherm batch case, which can be explained by the high crystallization rate caused by the high supersaturation. Contrary to this, in the non-isothermal batch case, the final mean particle size and variance are the lowest. In continuous mode, the variance and mean values are between the values obtained in the two other cases. In this case, the supersaturation is maintained at a constant level in the steady state, and the average residence time of the crystal particles also has an important influence on the crystal size distribution. In the case of non-isothermal crystallization, the simulation studies show that the application of the energy balance provides different dynamics for the crystallizers. The implementation of an energy balances into the mathematical model enables the calculation of the thermal behavior of the crystallizers, enabling the model to be used more widely.

R&D investments and employment decisions as a function of enterprise size and regional population density before and during COVID-19.

Norwegian data show that from 2018 to 2020, enterprises in densely populated regions increased R&D investments relative to those in sparsely populated regions, but not from 2016 to 2018. Therefore, COVID-19 likely induced the shift. The findings imply that densely populated regions have become more R&D-intensive, while sparsely populated regions have become less R&D-intensive during the pandemic. Small enterprises increased both R&D investments and employment from 2018 to 2020 relative to large enterprises and the analyses control for regression toward the mean effects. The findings were similar to those observed in the period from 2016 to 2018, which rules out COVID-19 as an explanation. Instead, the waves of data indicate a long-term trend where small enterprises increased R&D investments and employment.

Curcumin and 10-undecenoic acid as natural quorum sensing inhibitors of LuxS/AI-2 of Bacillus subtilis and LasI/LasR of Pseudomonas aeruginosa

The quorum sensing (QS) system is related to cell-to-cell communication as a function of population density, which regulates several physiological functions including biofilm formation and virulence gene expression. QS inhibitors have emerged as a promising strategy to tackle virulence and biofilm development. Among a wide variety of phytochemicals, many of them have been described as QS inhibitors. Driven by their promising clues, this study aimed to identify active phytochemicals against LuxS/autoinducer-2 (AI-2) (as the universal QS system) from Bacillus subtilis and LasI/LasR (as a specific QS system) of Pseudomonas aeruginosa, through in silico analysis followed by in vitro validation. The optimized virtual screening protocols were applied to screen a phytochemical database containing 3479 drug-like compounds. The most promising phytochemicals were curcumin, pioglitazone hydrochloride, and 10-undecenoic acid. In vitro analysis corroborated the QS inhibitory activity of curcumin and 10-undecenoic acid, however, pioglitazone hydrochloride showed no relevant effect. Inhibitory effects on LuxS/AI-2 QS system triggered reduction of 33–77% by curcumin (at 1.25–5 µg/mL) and 36–64% by 10-undecenoic acid (at 12.5–50 µg/mL). Inhibition of LasI/LasR QS system was 21% by curcumin (at 200 µg/mL) and 10–54% by 10-undecenoic acid (at 15.625–250 µg/mL). In conclusion, in silico analysis allowed the identification of curcumin and, for the first time, 10-undecenoic acid (showing low cost, high availability, and low toxicity) as alternatives to counteract bacterial pathogenicity and virulence, avoiding the imposition of selective pressure usually related to classic industrial disinfection and antibiotics therapy.

The Persistence of State-Listed Endangered and Threatened Seed Plants in Populated Illinois Landscapes

The state of Illinois has few natural areas remaining due to anthropogenic changes caused by agriculture, industry, and urbanization. This study investigates the seed plants at the greatest risk of extirpation from Illinois, the state-listed endangered and threatened species, and their occurrence relative to land use. The Illinois Endangered Species Protection Board and the Illinois Natural History Survey chronicle the occurrence and distribution of state-listed endangered and threatened species. We extracted the number of endangered/threatened species per county from these records. We also calculated the proportion of land cover per county for anthropogenic, non-anthropogenic, and protected areas. Using these data, we modeled the distribution of state-listed endangered and threatened seed plants as a function of land cover and human population density. Our study revealed that many endangered plant species are persisting in counties with high levels of human population density. A high degree of agricultural land cover was negatively associated with the presence of endangered/threatened seed plant species. We provide statistical evidence that endangered/threatened species are persistent in highly populated areas, given that there are protected lands with adequate habitat for them to grow. These results underscore the importance of protecting natural habitats. Urban development has preserved some of these habitats by establishing protected lands that allow the continued existence of many species. The role that protected lands play in the conservation of biodiversity is crucial to prevent extirpation of endangered/threatened seed plant species in Illinois.

Daily Covid-19 infected population densities in Italian provinces follow Taylor’s law

ABSTRACT Taylor’s law states that the spatial variance of the population density varies as the power function of the mean population density. This law is tested on daily Covid-19 infection density for five periods between February 25, 2020 and March 15, 2021. The Italian provinces are grouped by geography into three ensembles. A simultaneous-equation model accounts for correlations between the ensembles, between Italian provinces within each ensemble, and for temporal autocorrelations. The selected periods show ensembles with all Taylor’s law slopes below 2 (reflecting State interventions at the national level), or all above 2 (reflecting interventions at the local level), or some ensembles above while others were below. Slope of Taylor’s law and average density trend indicate whether the infection density is highly concentrated in a few provinces (when the slope is greater than 2 with increasing density, and when the slope is less than 2 with decreasing density) or spread evenly among all provinces in an ensemble (when the slope is greater than 2 with decreasing density, and when the slope is less than 2 with increasing density), which allows the government and epidemiologists to design disease control policies for targeted provinces and ensembles in Italy.

Characterization of Inclusion Size Distributions in Steel Wire Rods.

The control of inclusions in steel components is essential to guarantee strong performance. The reliable characterization of inclusion populations is essential not only to evaluate the quality of the components but also to allow the use of analytical procedures for the comparison and discrimination of inclusion populations. In this work, inclusion size distributions in wire rod specimens from six plant-scale heats were measured and analyzed. For the measurements, the metallographic procedure specified in the ASTM E2283 standard was used. The population density function (PDF) approach and the extreme value statistical procedure specified in the ASTM E2283 standard were used to analyze the whole size distribution and the upper tail of the size distribution, respectively. The PDF approach allowed us to identify differences among inclusion size distributions and showed that new inclusions were not formed after the liquid steel treatment process. The extreme value statistical procedure led to the prediction of the maximum inclusion length for each heat, which was used for the statistical discrimination of heats. Furthermore, the estimation of the probability of finding an inclusion larger than a given inclusion size using the extreme value theory allowed us to order the heats for different critical inclusion sizes.

Evolution of inclusion populations in Al-killed steel during the steelmaking process

The increasing demand for higher steel inclusion cleanliness has motivated control over the formation and evolution of inclusions during the production process. To generate knowledge of the formation and evolution of inclusions during the production of Al-killed steel slabs, the evolution of the chemical composition and size distribution of inclusions throughout the process, including ladle furnace (LF) treatment and continuous casting (CC), was studied. Liquid steel sampling was conducted throughout the process. The chemical composition was measured using the EDS technique, and the size distribution analysis was performed with the population density function (PDF) approach. The upper tail of the size distributions was analyzed by generalized extreme value (GEV) theory. Evolution of the chemical composition of inclusions involved the following path: solid Al2O3 solid Al2O3-MgO partly liquid Al2O3–MgO–CaO fully liquid Al2O3–MgO–CaO. The PDF analysis accounted for the evolution of the size distribution, which was supposed to be driven by the balance of the coalescence/growth and collision/breakage of inclusions and their removal. Finally, the GEV approach allowed us to describe the evolution of large inclusions and compare inclusion populations in terms of survival probability.

Improving Estimates of U.S. Tornado Frequency by Accounting for Unreported and Underrated Tornadoes

Abstract Many tornadoes are unreported because of lack of observers or are underrated in intensity, width, or track length because of lack of damage indicators. These reporting biases substantially degrade estimates of tornado frequency and thereby undermine important endeavors such as studies of climate impacts on tornadoes and cost–benefit analyses of tornado damage mitigation. Building on previous studies, we use a Bayesian hierarchical modeling framework to estimate and correct for tornado reporting biases over the central United States during 1975–2018. The reporting biases are treated as a univariate function of population density. We assess how these biases vary with tornado intensity, width, and track length and over the analysis period. We find that the frequencies of tornadoes of all kinds, but especially stronger or wider tornadoes, have been substantially underestimated. Most strikingly, the Bayesian model estimates that there have been approximately 3 times as many tornadoes capable of (E)F2+ damage as have been recorded as (E)F2+ [(E)F indicates a rating on the (enhanced) Fujita scale]. The model estimates that total tornado frequency changed little over the analysis period. Statistically significant trends in frequency are found for tornadoes within certain ranges of intensity, pathlength, and width, but it is unclear what proportion of these trends arise from changes in damage survey practices. Simple analyses of the tornado database corroborate many of the inferences from the Bayesian model. Significance Statement Prior studies have shown that the probabilities of a tornado being reported and of its intensity, track length, and width being accurately estimated are strongly correlated with the local population density. We have developed a sophisticated statistical model that accounts for these population-dependent tornado reporting biases to improve estimates of tornado frequency in the central United States. The bias-corrected tornado frequency estimates differ markedly from the official tornado climatology and have important implications for tornado risk assessment, damage mitigation, and studies of climate change impacts on tornado activity.

Dynamical analysis and optimal control of the developed information transmission model

Information transmission significantly impacts social stability and technological advancement. This paper compares the phenomenon of "Super transmission" and "Asymptomatic infection" in COVID-19 transmission to information transmission. The former is similar to authoritative information transmission individuals, whereas the latter is similar to individuals with low acceptance in information transmission. It then constructs an S2EIR model with transmitter authority and individual acceptance levels. Then, it analyzes the asymptotic stability of information-free and information-existence equilibrium on a local and global scale, as well as the model's basic reproduction number, R0. Distinguished with traditional studies, the population density function and Hamiltonian function are constructed by taking proportion of "Super transmitter" and proportion of hesitant group turning into transmitters as optimization control variables. Based on the Pontryagin maximum principle, an optimal control strategy is designed to effectively facilitate information transmission. The numerical simulation corroborates the theoretical analysis results and the system's sensitivity to control parameter changes. The research results indicate that the authoritative "Super transmitter" has a beneficial effect on information transmission. In contrast, the "Asymptomatic infected individual" with poor individual acceptance level negatively affects information transmission.

A Trait-based Investigation of Fungal Decomposition with Machine Learning

Fungi are of great functional significance in terrestrial ecosystems as the main decomposers. To better understand their decomposing process and population coexistence, we first describe and quantify the decomposition rate, focusing on three traits of interest selected by machine learning algorithm: moisture tolerance, hyper extension rate, and hyphal density and obtain, and use a Ternary Linear Regression Decomposition Model (TLRDM) to quantify the decomposition rate. Then, to incorporate the interactions, we build an Interactive Decomposition Model (IDM) and creatively employ a Three-player Logistic-based Competition Population Model (TPLCM). Based on logistic growth, we formulate a differential equation group, fit the curves of this unsolvable equation group to obtain a function of population density versus time and compare the decomposition rates of three populations under interactive and non-interactive conditions, followed by analyzing the impact of the communications on decomposing ability. We obtain the population combinations that can coexist in certain climates. Furthermore, we include environmental factors, conducting a sensitivity analysis to describe how short-term and long-term climate changes affect our models.

Individual-based and continuum models of phenotypically heterogeneous growing cell populations

<abstract> <p>Existing comparative studies between individual-based models of growing cell populations and their continuum counterparts have mainly been focused on homogeneous populations, in which all cells have the same phenotypic characteristics. However, significant intercellular phenotypic variability is commonly observed in cellular systems. In light of these considerations, we develop here an individual-based model for the growth of phenotypically heterogeneous cell populations. In this model, the phenotypic state of each cell is described by a structuring variable that captures intercellular variability in cell proliferation and migration rates. The model tracks the spatial evolutionary dynamics of single cells, which undergo pressure-dependent proliferation, heritable phenotypic changes and directional movement in response to pressure differentials. We formally show that the continuum limit of this model comprises a non-local partial differential equation for the cell population density function, which generalises earlier models of growing cell populations. We report on the results of numerical simulations of the individual-based model which illustrate how proliferation-migration tradeoffs shaping the evolutionary dynamics of single cells can lead to the formation, at the population level, of travelling waves whereby highly-mobile cells locally dominate at the invasive front, while more-proliferative cells are found at the rear. Moreover, we demonstrate that there is an excellent quantitative agreement between these results and the results of numerical simulations and formal travelling-wave analysis of the continuum model, when sufficiently large cell numbers are considered. We also provide numerical evidence of scenarios in which the predictions of the two models may differ due to demographic stochasticity, which cannot be captured by the continuum model. This indicates the importance of integrating individual-based and continuum approaches when modelling the growth of phenotypically heterogeneous cell populations.</p> </abstract>

Effect of Tundish Flux on Compositional Changes in Non-metallic Inclusions in Stainless Steel Melts

The effect of the tundish flux on the evolution of non-metallic inclusions in Si-killed 304 (18%Cr-8%Ni) stainless steel has been investigated at 1773 K. The interfacial reaction between molten steel and the CaO–Al2O3–MgO flux causes the aluminum pick-up from the liquid slag into the steel melt, resulting in a decrease in the oxygen content in the steel. The aluminum originating from the slag modifies the pre-existing Mn-silicate inclusions into alumina-rich inclusions in the steel. Because the oxygen content in the steel decreases as it reacts with the CaO–Al2O3–MgO flux, the degree of supersaturation for alumina formation is too low to precipitate new-born alumina particles in the steel. By analyzing the population density function (PDF) results for inclusions, it can be observed that the growth of spinel-type inclusions occurs by the diffusion of aluminum and magnesium in the steel. On the other hand, the composition of the steel, as well as the evolution of inclusions, is negligibly changed when the CaO–SiO2–MgO flux is added to the molten steel. Furthermore, the computational simulation for predicting the evolution of inclusions in molten steel during a continuous casting tundish process was carried out based on a refractory-slag-metal-inclusion (ReSMI) multiphase reaction model.

Evolutionary dynamics in an SI epidemic model with phenotype-structured susceptible compartment.

We present an SI epidemic model whereby a continuous structuring variable captures variability in proliferative potential and resistance to infection among susceptible individuals. The occurrence of heritable, spontaneous changes in these phenotypic characteristics and the presence of a fitness trade-off between resistance to infection and proliferative potential are explicitly incorporated into the model. The model comprises an ordinary differential equation for the number of infected individuals that is coupled with a partial integrodifferential equation for the population density function of susceptible individuals through an integral term. The expression for the basic reproduction number [Formula: see text] is derived, the disease-free equilibrium and endemic equilibrium of the model are characterised and a threshold theorem involving [Formula: see text] is proved. Analytical results are integrated with the results of numerical simulations of a calibrated version of the model based on the results of artificial selection experiments in a host-parasite system. The results of our mathematical study disentangle the impact of different evolutionary parameters on the spread of infectious diseases and the consequent phenotypic adaption of susceptible individuals. In particular, these results provide a theoretical basis for the observation that infectious diseases exerting stronger selective pressures on susceptible individuals and being characterised by higher infection rates are more likely to spread. Moreover, our results indicate that heritable, spontaneous phenotypic changes in proliferative potential and resistance to infection can either promote or prevent the spread of infectious diseases depending on the strength of selection acting on susceptible individuals prior to infection. Finally, we demonstrate that, when an endemic equilibrium is established, higher levels of resistance to infection and lower degrees of phenotypic heterogeneity among susceptible individuals are to be expected in the presence of infections which are characterised by lower rates of death and exert stronger selective pressures.