Constant Environment Research Articles

Global dynamics of a periodically forced SI disease model of Lotka–Volterra type

In this paper, we investigate the dynamics of an SI disease model of Lotka–Volterra type in the presence of a periodically fluctuating environment. We give a global analysis of the dynamical behavior of the model. Interestingly, our results show that the permanence guarantees the existence of a unique positive harmonic time-periodic solution which is globally attracting when the horizontal disease transmission has a weaker impact than the intraspecific competition. While for the case when the horizontal disease transmission has a stronger impact than the intraspecific competition, we numerically show that complex dynamics such as chaos can occur in a permanent system. Nonetheless, we provide sufficient conditions for the existence and uniqueness of the positive harmonic time-periodic solution for the latter case. The impact of the environment on the spread of disease is studied by using a bifurcation analysis. We show that in each of the qualitatively different cases of the associated autonomous SI model in a constant environment, an alternative possibility can appear in the periodic model.

Residence time prediction in magnetically controlled biomolecular local rebinding-dissociation kinetics

The residence time of drug-target conjugates is a critical factor in drug screening and efficacy prediction. The local rebinding-dissociation kinetics gives insights into in-vivo drug-target interactions. A magnetic torque system (MTS) is designed to observe rebinding-dissociation kinetics for predicting residence time. The system utilizes an alternating magnetic field (AMF) to manipulate the magnetization motion of magnetically labeled biomolecules and the forces acting upon biomolecular bonds. The motion, sensed by a quartz crystal microbalance (QCM), reflects biomolecular interactions occurring at the particle surface. Meanwhile, the motion facilitates the separation of dissociated molecules from the surface, thereby obviating the necessity for fixed and mobile phases in common kinetics observations. The constant and static solution environment minimizes reagent consumption. The MTS was utilized to observe the local rebinding-dissociation of antibodies (PAB and MAB) to magnetic beads (MB) and to HER2 receptors. The residence times recorded by the MTS were larger than the results obtained via SPR method, due to the occurrences of rebinding-dissociation kinetics. Interaction behaviours can be meticulously regulated for varying affinities by modulating the intensity of magnetic field. A high intensity field (400 Oe) was applied for strong binding between antibody-MB (biotin-streptavidin), and a low intensity field (300 Oe) was applied for weak antigen-antibody interactions. An increase in AMF strength enhanced dissociation, with a shift from 300 Oe to 400 Oe resulting in a 1 ∼ 4-fold reduction in residence time. Overall, the MTS provides an interactive and customizable perspective on kinetics observations.

Spatiotemporal fluctuation induces Turing pattern formation in the chemical Brusselator

Chemical reactions are embedded in spatiotemporal fluctuations instead of a constant environment. Here, we aimed to assess reaction–diffusion (RD) with dichotomous noise‐controlling system parameters in the Brusselator and examine the effect of these fluctuations on the dynamic behavior of chemical reactions. By performing a multiscale perturbation analysis, we demonstrated that the correlated noise can broaden the Turing region even if molecular memory (autocorrelation time) exists. However, for small noise, short‐term memory promotes Turing instability. The instability of the Brusselator is determined by the noise strength, which belongs to the optimal region if the diffusion coefficient is fixed. Turing pattern selection and stability are also governed by the dynamic character of the amplitude equation, and the entire Turing instability region shifts to the right in the phase space with noise perturbation. Finally, numerical simulations validate the theoretical derivation that correlated noise can amplify Turing pattern formation to maintain distinct patterns.

Optimal resource allocation in micro-organisms under periodic nutrient fluctuations

Although microorganisms often live in dynamic environments, most studies, both experimental and theoretical, are carried out under static conditions. In this work, we investigate the issue of optimal resource allocation in bacteria growing in periodic environments. We consider a dynamic model describing the microbial metabolism under varying conditions, involving a control variable quantifying the protein precursors allocation. Our objective is to determine the optimal strategies maximizing the long-term growth of cells under a piecewise-constant periodic environment. Firstly, we perform a theoretical analysis of the resulting optimal control problem (OCP), based on the application the Pontryagin’s Maximum Principle (PMP). We determine that the structure of the optimal control must be bang–bang, with possibly some singular arcs corresponding to optimal equilibria of the system. If the control presents singular arcs, then these can only be reached and left through chattering arcs. We also use a direct optimization method, implemented in the BOCOP software, to solve the studied OCP. Our study reveals that the optimal solution over a large time horizon is related to the one over a single period of the varying environment with periodic constraints. Moreover, we observe that the maximal average growth rate attainable under periodic conditions can be higher than the one under a constant environment. We further extend our analysis to conduct a qualitative comparison between the predictions from our model and some recent biological experiments on E. coli. This analysis particularly highlights the mechanisms of action of the ppGpp signaling molecule, thus providing relevant explanations of the experimental observations. In conclusion, our study corroborates previous research indicating that this molecule plays a crucial role in the regulation of resource allocation of protein precursors in E. coli.

Innovation in viruses: fitness valley crossing, neutral landscapes, or just duplications?

Viruses evolve by periods of relative stasis interleaved with sudden, rapid series of mutation fixations, known as evolutionary bursts. These bursts can be triggered by external factors, such as environmental changes, antiviral therapies, or spill-overs from reservoirs into novel host species. However, it has also been suggested that bursts may result from the intrinsic evolutionary dynamics of viruses. Indeed, bursts could be caused by fitness valley crossing, or a neutral exploration of a fitness plateau until an escape mutant is found. In order to investigate the importance of these intrinsic causes of evolutionary bursts, we used a simulation software package to perform massive evolution experiments of viral-like genomes. We tested two conditions: (i) after an external change and (ii) in a constant environment, with the latter condition guaranteeing the absence of an external triggering factor. As expected, an external change was almost systematically followed by an evolutionary burst. However, we also observed bursts in the constant environment as well, albeit much less frequently. We analyzed how many of these bursts are triggered by deleterious, quasi-neutral, or beneficial mutations and show that, while bursts can occasionally be triggered by valley crossing or traveling along neutral ridges, many of them were triggered by chromosomal rearrangements and, in particular, segmental duplications. Our results suggest that combinatorial differences between the different mutation types lead to punctuated evolutionary dynamics, with long periods of stasis occasionally interrupted by short periods of rapid evolution, akin to what is observed in virus evolution.

Dynamics of a SVEIR Epidemic Model with a Delay in Diagnosis in a Changing Environment

A SVEIR epidemic model with a delay in diagnosis is studied in a constant and variable environment. The mathematical analysis shows that the dynamics of the model in the constant environment are completely determined by the magnitude of the delay-induced reproduction number . We established that if < 1, the disease-free equilibrium is globally asymptotically stable, and when > 1 the endemic equilibrium is globally asymptotically stable. In the variable environment, the model undergoes a transcritical bifurcation for = 1 leading to changes in the stability of the equilibrium points. The analytical effect of the delays in epidemic diagnosis is investigated. A minimum diagnosis rate αmin has been determined to face or control the disease effectively. Finally, numerical illustrations were presented to support the theoretical results.

ILAM: Imaging Locomotor Activity Monitor for circadian phenotyping of large‐bodied flying insects

Abstract Historically, most insect chronoecological research has used direct observations, cameras or infrared beam‐based monitors to quantify movement across timed intervals. Although some alternative DIY systems are cheaper than the current standard locomotor activity monitor, these options remain complicated to build and/or computationally intensive. We developed the imaging Locomotor Activity Monitor (iLAM), an affordable (~$75 USD/unit) system for activity quantification. The iLAM utilizes a Raspberry Pi Zero W computer and night‐vision camera inside a flight cage to photograph a population of insects at user‐defined intervals. Open‐source, modular R‐scripts process the images and output a file containing the number, size, coordinate location and timestamp of all movements (blobs) identified between consecutive images. Output can be analysed directly or converted into the standard TriKinetics DAM format. We demonstrated the flexibility and power of the iLAM system by comparing diel and circadian activity of different insect species (fireflies: Photinus marginellus, P. greeni, P. obscurellus), ecotypes (moths: Ostrinia nubilalis) and sexes (moths: O. nubilalis). Data captured by only six iLAMs ($450) identified that peak activity of O. nubilalis females (AZT: 19.2 h) occurs significantly earlier than males (22.0 h). Additionally, male moths from a univoltine population exhibited a significantly shorter endogenous period length (AZT: 21.3 h) than males from a bivoltine genetic background (22.7 h). The iLAM will serve as a valuable tool for researchers seeking to measure locomotor activity across diverse species, sexes and populations in constant and changing environments.

Effect of Experimental Parameters on the Electrochemical Current during Scanning Electrochemical Microscopy of Living Cells

Scanning Electrochemical Microscopy (SECM) is gaining popularity for its application to live cell imaging to monitor electrochemical processes occurring at the interface of living cells and electrodes. [1] Although many studies have been reported varying the instrumental parameters and using various probe designs, [2] the influence of experimental parameters on the electrochemical current must not be neglected, in order to generate reliable results. Our work explores parameters, such as temperature, light exposure and cell media during the SECM analysis of Adenocarcinoma Cervical cancer (HeLa) cells. The experimental and numerical studies with temperature variation revealed that temperature changes of 2ᵒC influence the reaction kinetics of single living cells. [3] Further, it was observed during light exposure that the electrochemical current becomes stabilized after 20 minutes of constant light environment compared to dark, where the standard deviation of the electrochemical current showed an increase in trend overtime. Furthermore, electrochemical current responses of HeLa cells in Phosphate Buffered Solution (PBS), Dubelcco’s Modified Eagle Medium (DMEM) and DMEM with 10% Fetal Bovine Serum (FBS) is being explored. These results will help to understand the origin of potential electrochemical current changes and the ideal experimental conditions to perform SECM measurements with living cells.[1] I. Beaulieu, S. Kuss, J. Mauzeroll, M. Geissler, Biological scanning electrochemical microscopy and its application to live cell studies, Anal. Chem. 83 (2011) 1485–1492. https://doi.org/10.1021/ac101906a.[2] D. Polcari, P. Dauphin-Ducharme, J. Mauzeroll, Scanning Electrochemical Microscopy: A Comprehensive Review of Experimental Parameters from 1989 to 2015, Chem. Rev. 116 (2016) 13234–13278. https://doi.org/10.1021/acs.chemrev.6b00067.[3] N. Thomas, D. Lima, D. Trinh, S. Kuss, Temperature Effect on the Electrochemical Current Response during Scanning Electrochemical Microscopy of Living Cells, Anal Chem. 95 (2023) 17962-17967. https://doi.org/10.1021/acs.analchem.3c03716

Effect of antioxidants on the efficiency of jet milling and the powder characteristics of Sm2Co17 permanent magnets

Abstract This study investigated the effect of antioxidants on the grinding efficiency, magnetic powder characteristics, microstructure, and magnetic properties of 2:17 type SmCo permanent magnet materials. The results show that adding antioxidants helps improve the dispersion among magnetic powders, leading to a 33.3% decrease in jet milling time and a 15.8% increase in magnet powder production yield. Additionally, adding antioxidants enhances the oxidation resistance of the magnetic powders. After being stored in a constant temperature air environment at 25 °C for 48 h, the O content in the powder decreased by 33% compared to samples without antioxidants. While in the magnet body, the O content decreased from 0.21 wt.% to 0.14 wt.%, which helps increase the effective Sm content and domain wall pinning uniformity in the magnet. Excellent magnetic properties were obtained in the magnet with added antioxidants: B r = 11.6 kGs, SF = 79.6%, H cj = 16.8 kOe, and (BH)max = 32.5 MGOe.

Shape memory polymer lattice structures with programmable thermal recovery time

Shape memory polymer (SMP) lattice structures have garnered considerable attention due to their intrinsic capability for self-recovery and mechanical reconfiguration. The temporal path, encompassing aspects such as recovery time and deployment sequence, of the shape recovery process in SMP lattice structures holds paramount significance across various domains, including but not limited to medical devices and space deployable structures. Nonetheless, the programming of shape recovery time or deployment sequences in SMP lattice structures, particularly in scenarios devoid of external controllers, remains a challenge. In addressing these challenges, this study presents a novel class of SMP structures endowed with customizable thermal recovery times and programmable deployment sequences, leveraging the influence of structural geometry. Notably, the programmable recovery time and serialized deployment behavior of the proposed SMP lattice structure are achieved within a constant temperature environment, obviating the need for external time-varying stimuli. Finite element simulations and experimental validations corroborate that the proposed SMP structures can be programmed to exhibit recovery times spanning from mere seconds to several hundred seconds. Moreover, a three-stage sequential recovery behavior is attained without necessitating prior local configuration programming. Furthermore, exploiting the distinctive sequential reversibility inherent in a constant high-temperature environment, the designed lattice structure showcases the ability to transition to multiple distinct stable configurations by modulating the duration of high-temperature exposure. The proposed recovery time programmable SMP lattice structure thus presents a viable avenue for realizing intricate multistage controllable shape-shifting structures devoid of external control equipment.

Novel copper-based metal–organic skeleton smart tags that respond to ammonia for real-time visual freshness monitoring of shrimp

In recent years, an amount of attention has been paid to natural pigment-based pH/ammonia-responsive colorimetric smart labels in the field of real-time visual food freshness monitoring. However, the drawbacks of natural pigments, such as complicated extraction processes, poor photostability, and easy inactivation, have severely limited their application in smart packaging. In this research, natural pigments were replaced with copper-based metal–organic frameworks (Cu-MOF) to serve as ammonia-responsive indicators. It attained the benefits of excellent mechanical qualities, high photostability, and greenness. The changes of ΔE values of the smart label composite films of starch/polyvinyl alcohol (PVA)/Cu-MOF were less than 4.8 after being placed in a constant temperature and humidity environment for 40 d, indicating that they have excellent color stability. Within an atmosphere of 0.8 mM ammonia, the composite film can produce a significant color change from teal green to deep blue in 1 min, indicating that the film has ammonia sensitivity. The composite film, in addition, successfully monitored the changes in shrimp freshness at temperatures of 4 °C and 28 °C, effectively prolonging the shelf life of the shrimp. Therefore, starch/ PVA/Cu-MOF composite films have good prospects for smart and active packaging applications.

EFEKTYWNOS´ C REGULACJI HOMEOSTAZY ELEKTROLITOWEJ KRWI LISÓW POLARNYCH ´ (ALOPEX LAGOPUS, LINNAEUS, 1758)

The Arctic fox (Alopex lagopus, L. 1758) is maintained in captivity for breeding purposes due to the high quality of its winter fur. The success of breeding these animals depends primarily on their health, which determines high reproductive rates, low neonatal mortality and proper development of young animals. A prerequisite for maintaining good health is the maintenance of a constant internal environment, especially water-electrolyte and acid-base homeostasis. Sodium, potassium and chloride are the main electrolytes affecting volemia and osmolality of body fluids. In the current study, the results regarding the renal capacity of six-month-old foxes to regulate sodium, potassium, and chloride levels are presented, considering the sex of these animals. It has been shown that the concentration of sodium, potassium, and chloride in the blood serum of six-month-old polar foxes, as well as the osmotic pressure of the blood serum, were within the upper limits of physiological normal range. Higher concentrations of sodium and potassium were observed in males of these animals. Nonetheless, the results indicated efficient regulation of blood electrolyte homeostasis, both in females and males.

Real-time quantitative detection of hydrocolloid adulteration in meat based on Swin Transformer and smartphone.

Hydrocolloids are widely used in meat products as common food additives. However, research has indicated that excessive consumption of these hydrocolloids may have potential health implications. Currently, consumers mainly rely on sensory evaluation to identify hydrocolloid adulteration in meat products. Although many studies on quantitative detection of hydrocolloids have been conducted by biochemical methods in laboratory environments, there is currently a lack of effective tools for consumers and regulators to obtain real-time and reliable information on hydrocolloid adulteration. To address this challenge, a smartphone-based computer vision method was developed to quantitatively detect carrageenan adulteration in beef in this work. Specifically, Swin Transformer models, along with pre-training and fine-tuning techniques, were used to successfully automate the classification of beef into nine different levels of carrageenan adulteration, ranging from 0% to 20%. Among the tested models, Swin-Tiny (Swin-T) achieved the highest trade-off performance, with a Top-1 accuracy of 0.997, a detection speed of 3.2ms, and a model size of 103.45 Mb. Compared to computer vision, the electrochemical impedance spectroscopy achieved a lower accuracy of 0.792 and required a constant temperature environment and a waiting time of around 30 min for data stabilization. In addition, Swin-T model was also capable of distinguishing between different types of hydrocolloids with a Top-1 accuracy of 0.975. This study provides consumers and regulators with a valuable tool to obtain real-time quantitative information about meat adulteration anytime, anywhere. PRACTICAL APPLICATION: This research provides a practical solution for regulators and consumers to non-destructively and quantitatively detect the content and type of hydrocolloids in beef in real-time using smartphones. This innovation has the potential to significantly reduce the costs associated with meat quality testing, such as the use of chemical reagents and expensive instruments.

Phenotypic plasticity and the effects of thermal fluctuations on specialists and generalists.

Classical theories predict that relatively constant environments should generally favour specialists, while fluctuating environments should be selected for generalists. However, theoretical and empirical results have pointed out that generalist organisms might, on the contrary, perform poorly under fluctuations. In particular, if generalism is underlaid by phenotypic plasticity, performance of generalists should be modulated by the temporal characteristics of environmental fluctuations. Here, we used experiments in microcosms of Tetrahymena thermophila ciliates and a mathematical model to test whether the period or autocorrelation of thermal fluctuations mediate links between the level of generalism and the performance of organisms under fluctuations. In the experiment, thermal fluctuations consistently impeded performance compared with constant conditions. However, the intensity of this effect depended on the level of generalism: while the more specialist strains performed better under fast or negatively autocorrelated fluctuations, plastic generalists performed better under slow or positively autocorrelated fluctuations. Our model suggests that these effects of fluctuations on organisms' performance may result from a time delay in the expression of plasticity, restricting its benefits to slow enough fluctuations. This study points out the need to further investigate the temporal dynamics of phenotypic plasticity to better predict its fitness consequences under environmental fluctuations.

Model-based process optimization of black soldier fly egg production.

Black soldier fly (BSF) larvae (Hermetia illucens) serve as a valuable protein source for animal feed. Limiting factors in the industrial rearing of BSF are the reproduction process and egg output. Studies indicate the potential to shorten preoviposition time and increase egg output through better utilization of environmental variables, such as temperature and light, in industrial settings. Excessive stimulation, however, can lead to stress, elevated production costs, and reduced egg numbers, emphasizing the need for a delicate balance. This study addresses these challenges by investigating controlled manipulation of environmental variables to stimulate mating and enhance egg production, thereby developing a comprehensive model encompassing the adult fly life cycle, mating, and egg production. Model parameters were fitted using literature data, and the model's plausibility was tested through simulations. Using the model and optimal control methods, the calculated dynamic trajectories for environmental variables when compared to the standard approach in a constant environment demonstrated higher output and shorter production cycles at reasonable energy costs. Applications for this model-based optimization are demonstrated for various scenarios, highlighting the practical utility and versatility of the developed model. This study contributes valuable insights for improving rearing practices of BSF through environmental stimulation, offering potential advancements in egg production efficiency and overall sustainability.

Polymorphism and the Red Queen: the selective maintenance of allelic variation in a deteriorating environment.

Although allelic variation is ubiquitous in natural populations, our theoretical models are poor at predicting the existence and properties of these observed polymorphisms. In this study, inspired by Van Valen's Red Queen hypothesis, we modeled the effect of viability selection in a deteriorating environment on the properties of allelic variation in populations subject to recurrent mutation. In Monte Carlo simulations, we found that levels of polymorphism consistently built up over time. We censused the simulated populations after 10,000 generations of mutation and selection, revealing that, compared with models assuming a constant environment, the mean number of alleles was greater, as was the range of allele numbers. These results were qualitatively robust to the addition of genetic drift and to the relaxation of the assumption that the viabilities of phenogenotypes containing a new mutation are independent of each other (i.e. incorporating a model of generalized dominance). The broad range of allele numbers realized in the simulated populations-from monomorphisms to highly polymorphic populations-more closely corresponds to the observed range from numerous surveys of natural populations than previously found in theoretical studies. This match suggests that, contrary to the views of some writers, selection may actively maintain genetic variation in natural populations, particularly if the selective environment is gradually becoming harsher. Our simulations also generated many populations with heterozygote advantage, a mismatch with real data that implies that this selective property must arise extremely rarely in natural populations.

Dynamics of the generalized Rosenzweig–MacArthur model in a changing and patchy environment

In this paper, we propose a generalized Rosenzweig–MacArthur predator–prey model with two patches and Allee effects in predators. In a constant environment, for the single-patch model we show the existence of degenerate Bogdanov–Takens bifurcation with codimension up to 4 and degenerate Hopf bifurcation with codimension up to 3, which indicate that the nilpotent cusp of codimension 4 is the organizing center of the bifurcation set. Our results show that Allee effects in predators can not only induce much more complex bifurcations and dynamics (such as three limit cycles, multitype tristability), but also serve as a destabilizing factor to make predators extinction (deterministically) when the environment is sufficiently enriched, which provides strong support for the so-called “paradox of enrichment”. For the two-patch model, we numerically reveal the existence of eight positive steady states including two new coexisting attractors, which means that population dispersal can help species coexist or increase the population abundance. In a changing environment, for the single-patch model we find that the population can track unstable states, fast positive or slow negative environmental change can be beneficial to predators by delaying or even avoiding extinction; while for the two-patch model we observe some interesting phenomena: fast negative (or slow positive) environmental change can make predators in different patches trend to extinction synchronously, and rate-induced different regime shifts.

A numerical determination of complex solid gun propellant burn rates through closed bomb simulation

AbstractClosed bomb testing is a prominent means of characterizing the combustion behavior of solid gun propellants. This sub‐scale test allows the propellant to burn in a constant volume environment, where the resulting pressure‐time trace can be collected via a pressure transducer. Historically, numerical procedures have been developed to determine the burn rates of the gun propellants from these pressure‐time traces; however, no standardized procedure exists to determine the burn rates of grains with variable surface thermochemistry and ignition criteria. To address this capability gap, a non‐linearly constrained, multivariate optimization algorithm has been developed to decouple propellant grain surfaces and determine surface‐specific burn rates [1]. In this work, the optimization algorithm as well as the legacy Excel‐based Closed Bomb (XLCB) program [2] were used to determine the burn rates of homogeneous, deterred, and layered propellants from experimental data. Closed bomb simulations using these burn rates were then conducted with the two‐phase, multidimensional, interior ballistics solver, iBallistix [3]. The maximum mean error between the simulated and experimental pressure‐time curves was 6.8 % for the optimization algorithm and 23.8 % for XLCB, showing a marked improvement with our new approach. Furthermore, the approach discussed herein improves burn rate predictions of complex solid gun propellants when compared with legacy closed bomb data reduction analysis programs.

Representational drift as a result of implicit regularization

Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; and (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.

Representational drift as a result of implicit regularization.

Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; and (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.