Real Organisms Research Articles

Feisty fish and birds with attitude: Why does evolution not lead to identical individuals?

Natural selection is often thought to “perfect” an organism. Yet, real organisms show lots of diversity in their behaviours and morphology. We show that competition for resources can either maintain trait diversity at a constant high level, or lead to wild cycles in trait evolution. Continuous differences in resources like those found in nature are an important part of these outcomes.

“Silver effect” enhanced fluorescence for sensitive detection of crystal violet utilizing long wavelength emission bimetallic gold-silver nanoclusters

Crystal violet (CV), widely used as a fungicide in aquaculture, is susceptible to enrichment in fish and thus poses a threat to human health. Therefore, there is an urgent need for a simple, sensitive and cost-effective method to detect CV. To this need, duck egg white-stabilized gold-silver nanoclusters (DEW-AuAgNCs) with long wavelength fluorescence emission were synthesized via microwave method in 1 min. The raw materials used were easily available and environmentally friendly. And based on the Fluorescence resonance energy transfer (FRET) between DEW-AuAgNCs and CV, a fluorescence quenching method was established for CV detection. The detection limit of this method was 1.7 × 10−8 mol/L, which was lower than most of the reported methods. In addition, prominent performance of DEW-AuAgNCs with long wavelength emission can be applied for the detection of CVs in real organism.

Development of a near-infrared ratiometric fluorescent probe for real-time detection of HClO in real samples, oxidative stress model, and ferroptosis model

During the COVID-19 epidemic, hypochlorous acid (HClO) was frequently utilized for indoor and outdoor disinfection. The sensitive and selective detection of HClO puts forward high requirements for food safety. Furthermore, HClO is crucial for physiological and pathological activities. However, excessive HClO is harmful to the environment, animals, and humans. Therefore, it is imperative to construct a highly selective and sensitive HClO detection method to determine the safety of food and water, as well as the role of physiological and pathological processes. Herein, we successfully developed a near-infrared (NIR) ratiometric fluorescent probe FLD, which had high practicality and wide application range and was utilized to detect HClO in real samples and organisms. Probe FLD had the advantages of simple synthesis, high selectivity and sensitivity, significant colorimetric change, and low detection limit. Probe FLD was loaded onto test strips to create a single, reasonably priced solid sensor that enabled the visual detection of HClO. The experiments with real samples showed that the probe had good accuracy and precision. Importantly, the measurement of HClO levels in oxidative stress and ferroptosis models was successfully accomplished. Overall, probe FLD’s outstanding qualities make it a good prospect for application in environmental monitoring, food safety, and biology.

Features of chemical elements migration in natural waters and their deposition in the form of neocrystallisations in living organisms (physico-chemical modeling with animal testing)

Relevance. An original modeling method is presented, which allows obtaining qualitatively new results in practice of ecological-geochemical and biomedical research. Aim. To obtain new data on the forms of element migration in the conditions of animal and human organism parameters by means of physicochemical modeling (PC "Selector") with verification of calculated data with the results obtained for real natural objects and animal organisms. Objects. Samples of natural waters and tissues of wild animals. Methods. Computer modeling (PC "Selector"); inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7700x spectrometer) – 55 elements; atomic emission spectrometry (AES, spectrometer iCAP 7600 Duo) –- 5 macrocations; ion chromatography (IC, ion-liquid chromatograph LC-20) – 6 anions; inductively coupled plasma mass spectrometry (ICP-MS, mass spectrometer NexION 300D; scanning electron microscopes Hitachi S-3400N with Bruker X@Flash 5010 energy dispersive spectrometer. Results. By means of modeling, the authors have determined the qualitative and quantitative composition of the system "solution – crystalline substance". They took into account environmental conditions and physiological parameters of animal and human organism. Natural drinking water, gastric juice, mixture of drinking water and gastric juice were used as a solution, and crystalline substance was used as newly formed mineral phases in equilibrium with the solution. The work allowed establishing that the complexes assumed in the model experiment can be the cause of appearance of nanomineral phases of chemical elements in separate tissues of a living organism.

Genetic visual technologies and cellular automates

The phenomenology of self-organization based on the four rules in Conway’s game “Life” and the phenomenology of biological life based on the four nucleotides has several parallels and unresolved issues. A cellular automaton operating on a two-dimensional plane is proposed as a basic object for joint consideration of these phenomena. In one case, its dynamics are set by local/global self-organization rules based on the initial configuration in an automaton of the “Life” type of game. In another case, it is a set of nucleotides in the DNA of a particular organism. The author considers images similar to cellular automata generated by the DNA of real organisms when constructing geometric patterns on a two-dimensional plane by changing the mapping scaling parameter. With increasing scaling parameters, individual clusters emerge for living organisms, which can be located symmetrically.

Central pattern generators for biomorphic robotics

Typically, the structure of the robot fish frame significantly differs compared to the real organism. One significant difference is in the number of body segments. While live fish can have between 16 (moon fish) to 400 belt fish [1] segments, robots usually have only 5–6 segments since substantial precision is unnecessary when simulating movement. At the same time, this method limits a significant portion of the control circuit’s structure compared to a fish’s nervous system because it only requires control over a smaller number of body segments. Control systems using different oscillators can simulate the functioning of fish central generators [2–4]. Typically, each half-center of the fish’s CPGs is interconnected with and mutually inhibitory towards the others, with each being responsible for the antagonist muscles. In this case, the generator’s pattern characteristics stem from the mutual influence of oscillator-antagonist pairs connected to each other. The half-centers’ interaction mechanism with each other is designed to match the movement pattern’s desired final parameters. This “artificial” approach is unsuitable for working with spike neurons because the mechanisms of cellular interaction are well-defined. Altering how cells interact with each other when creating a biologically relevant model is also undesired. Here we present evidence that incorporating select physiological traits of fish into the design of a CPG structure utilizing spike neurons can enhance the system’s functional capacity. Previously, we demonstrated a half-center CPG model using Izhikevich neurons [5]. This model can serve as a control loop for a tuna robot. Although this development aligns with the fundamental principles of CPG organization in fish, reproducing the typical generator mode of operation for pike on it proved challenging. This is due to the fact that the anguliform type of locomotion implies the presence of a moving wave, which means a phase lag in the activation of half-centers. One potential solution to the problem lies in the physiology of fish, specifically the structure of their muscle fibers. Fish have muscle segments called myomeres, which correlate with the number of vertebrae and spinal centers that create the CPG. A distinguishing feature of these myomeres is their zigzag shape. To achieve body bending at a single point, it requires a collaborative action between multiple myomeres and corresponding CPG segments. While our model assumes control of the entire fish with only 5 segments of the CPG, the actual pike includes 56–65 segments. To attain the necessary difference in activation phase between generator segments, we propose increasing the number of generator nodes responsible for operating a single propulsion unit. Indeed, increasing the number of transmission segments resulted in a steady divergence in activation phases among successive segments of the CPG. However, the incorporation of supplementary segments fails to address the challenge of managing the frequency of the CPG’s operation and shifting between patterns. Consequently, we intend to integrate CPG neurons of diverse types outlined in the model, along with introducing feedback to rectify its modes of operation in the future.

A Substituted-Rhodamine-Based Reversible Fluorescent Probe for In Vivo Quantification of Glutathione.

Quantifying glutathione (GSH) in cells and organisms is of great significance for understanding the mechanism of oxidative stress in various physiological and pathological processes. However, the quantification by fluorescence bioimaging in living tissues has much stricter requirements than the "Petri dish"-cultured cells in flat plates. Based on the evaluation of the electronic structure and steric hindrance-tuned reactivity of phospha-substituted rhodamine with GSH, a reversible Förster resonance energy transfer (FRET) probe ZpSiP with a distinct performance (Kd =4.9 mM, t1/2 =0.57 s, k=81 M-1 s-1 ) is developed for real time quantifying GSH in living cells. Furthermore, the near-infrared (NIR) probe succeeded in sensitively tracking the dynamics of GSH in the real organisms bearing tumors, chronic renal failure, and liver fibrosis for unveiling the related pathological processes. We believe that the advance in chemistry with quantitative analysis methods will initiate more promising progress and broad applications.

A Substituted‐Rhodamine‐Based Reversible Fluorescent Probe for In Vivo Quantification of Glutathione

AbstractQuantifying glutathione (GSH) in cells and organisms is of great significance for understanding the mechanism of oxidative stress in various physiological and pathological processes. However, the quantification by fluorescence bioimaging in living tissues has much stricter requirements than the “Petri dish”‐cultured cells in flat plates. Based on the evaluation of the electronic structure and steric hindrance‐tuned reactivity of phospha‐substituted rhodamine with GSH, a reversible Förster resonance energy transfer (FRET) probe ZpSiP with a distinct performance (Kd=4.9 mM, t1/2=0.57 s, k=81 M−1 s−1) is developed for real time quantifying GSH in living cells. Furthermore, the near‐infrared (NIR) probe succeeded in sensitively tracking the dynamics of GSH in the real organisms bearing tumors, chronic renal failure, and liver fibrosis for unveiling the related pathological processes. We believe that the advance in chemistry with quantitative analysis methods will initiate more promising progress and broad applications.

Foraging Dynamics and Entropy Production in a Simulated Proto-Cell.

All organisms depend on a supply of energetic resources to power behavior and the irreversible entropy-producing processes that sustain them. Dissipative structure theory has often been a source of inspiration for better understanding the thermodynamics of biology, yet real organisms are inordinately more complex than most laboratory systems. Here we report on a simulated chemical dissipative structure that operates as a proto cell. The simulated swimmer moves through a 1D environment collecting resources that drive a nonlinear reaction network interior to the swimmer. The model minimally represents properties of a simple organism including rudimentary foraging and chemotaxis and an analog of a metabolism in the nonlinear reaction network. We evaluated how dynamical stability of the foraging dynamics (i.e., swimming and chemotaxis) relates to the rate of entropy production. Results suggested a relationship between dynamical steady states and entropy production that was tuned by the relative coordination of foraging and metabolic processes. Results include evidence in support of and contradicting one formulation of a maximum entropy production principle. We discuss the status of this principle and its relevance to biology.

Effects of Bionic Bone Flexibility on the Hydrodynamics of Pectoral Fins

Compared with traditional underwater equipment powered by propeller, the manta-ray-inspired vehicle with MPF mode (Median fin/paired fin) has the advantages of stable swimming attitude, high maneuverability, and low noise, etc. As one of the sources of advancing power when the manta-ray-inspired vehicle swims, the flexible deformation of the pectoral fin is an important factor affecting the hydrodynamic performance. In this paper, a mechanical analysis of the two-dimensional flexible pectoral fin using thin wing theory shows that the main factor affecting the hydrodynamic force of the two-dimensional flexible pectoral fin is the level of curvature of the pectoral fin chordal section. By designing a two-stage bionic skeleton at the leading and rear edges of the manta-ray-inspired vehicle, the root–tip section width of the bionic skeleton is used to characterize the level of the bionic pectoral fin’s flexibility, and a tensiometer is used to quantitatively measure the level of flexibility. The root-to-tip ratio of the cross-section was varied to obtain different levels of pectoral fin flexibility, and the hydrodynamic properties of the pectoral fins during flapping were measured using a force sensor and normalized for analysis. The experimental results show that the reduction of the flexibility of the leading edge and the increase of the flexibility of the rear edge are beneficial to the improvement of the thrust performance, and the experimental results are the same as the distribution of the skeletal flexibility in real organisms. Fitting curves of the pectoral fins’ relative flexibility and the normalized thrust/lift show that the flexibility of the pectoral fins has a significant effect on its hydrodynamic force, and a stiffer leading edge and a softer rear edge can improve the hydrodynamic characteristics of the manta-ray-inspired vehicle. Phase differences interacting with flexibility can also enhance bionic pectoral fins’ dynamic properties within 10~30 degree.

Randomness in the choice of neighbours promotes cohesion in mobile animal groups.

Classic computational models of collective motion suggest that simple local averaging rules can promote many observed group-level patterns. Recent studies, however, suggest that rules simpler than local averaging may be at play in real organisms; for example, fish stochastically align towards only one randomly chosen neighbour and yet the schools are highly polarized. Here, we ask—how do organisms maintain group cohesion? Using a spatially explicit model, inspired from empirical investigations, we show that group cohesion can be achieved in finite groups even when organisms randomly choose only one neighbour to interact with. Cohesion is maintained even in the absence of local averaging that requires interactions with many neighbours. Furthermore, we show that choosing a neighbour randomly is a better way to achieve cohesion than interacting with just its closest neighbour. To understand how cohesion emerges from these random pairwise interactions, we turn to a graph-theoretic analysis of the underlying dynamic interaction networks. We find that randomness in choosing a neighbour gives rise to well-connected networks that essentially cause the groups to stay cohesive. We compare our findings with the canonical averaging models (analogous to the Vicsek model). In summary, we argue that randomness in the choice of interacting neighbours plays a crucial role in achieving cohesion.

Toward a Logic of the Organism: A Process Philosophical Consideration.

Mathematical models applied in contemporary theoretical and systems biology are based on some implicit ontological assumptions about the nature of organisms. This article aims to show that real organisms reveal a logic of internal causality transcending the tacit logic of biological modeling. Systems biology has focused on models consisting of static systems of differential equations operating with fixed control parameters that are measured or fitted to experimental data. However, the structure of real organisms is a highly dynamic process, the internal causality of which can only be captured by continuously changing systems of equations. In addition, in real physiological settings kinetic parameters can vary by orders of magnitude, i.e., organisms vary the value of internal quantities that in models are represented by fixed control parameters. Both the plasticity of organisms and the state dependence of kinetic parameters adds indeterminacy to the picture and asks for a new statistical perspective. This requirement could be met by the arising Biological Statistical Mechanics project, which promises to do more justice to the nature of real organisms than contemporary modeling. This article concludes that Biological Statistical Mechanics allows for a wider range of organismic ontologies than does the tacitly followed ontology of contemporary theoretical and systems biology, which are implicitly and explicitly based on systems theory.

Uneven Resources network promotes cooperation in the prisoner's dilemma game

Scholars explored cooperative behavior on different network; however they did not consider the resource distribution and consumption on them. In our work, we propose a kind of uneven resource distribution network model, in which players can consume finite resources to survive. In our model, firstly, there are four players, and then we imitate real organism behavior, such as eating, migration, game, leaning, and reproduction. Meanwhile, during this process, finite resource also decreases with players’ consumption. After numerical simulation, we find that defectors can occupy the most resource-rich areas at first, but they cannot survive in hostile area and decrease rapidly. While cooperators always keep growing. In addition, when resources decrease substantially, cooperative behavior can be promoted. This also sprouts new insights on survival and development of cooperative behavior.

Microbial Taxonomy Run Amok.

DNA sequencing has led to an explosion in discovery of microbial phylogenetic novelty, especially that represented by uncultivated taxa, to which the traditional system of prokaryotic taxonomy has not adapted. A lack of expansion of the International Code of Nomenclature of Prokaryotes (ICNP, 'the Code') to effectively capture this information has created a 'wild west' situation where names are published or appear in popular reference databases without further verification or validation. The rapid propagation of variant and questionable naming methods has led to widespread confusion and undermines prior accomplishments. We exemplify inconsistencies that have arisen from this practice and endanger the interoperability of scientific findings. The immediate solution to this problem is to develop and adopt universal best practices that are accepted by expert researchers, major publishers, the International Committee on Systematics of Prokaryotes (ICSP), and international microbiological societies.

A generative spiking neural-network model of goal-directed behaviour and one-step planning.

In mammals, goal-directed and planning processes support flexible behaviour used to face new situations that cannot be tackled through more efficient but rigid habitual behaviours. Within the Bayesian modelling approach of brain and behaviour, models have been proposed to perform planning as probabilistic inference but this approach encounters a crucial problem: explaining how such inference might be implemented in brain spiking networks. Recently, the literature has proposed some models that face this problem through recurrent spiking neural networks able to internally simulate state trajectories, the core function at the basis of planning. However, the proposed models have relevant limitations that make them biologically implausible, namely their world model is trained ‘off-line’ before solving the target tasks, and they are trained with supervised learning procedures that are biologically and ecologically not plausible. Here we propose two novel hypotheses on how brain might overcome these problems, and operationalise them in a novel architecture pivoting on a spiking recurrent neural network. The first hypothesis allows the architecture to learn the world model in parallel with its use for planning: to this purpose, a new arbitration mechanism decides when to explore, for learning the world model, or when to exploit it, for planning, based on the entropy of the world model itself. The second hypothesis allows the architecture to use an unsupervised learning process to learn the world model by observing the effects of actions. The architecture is validated by reproducing and accounting for the learning profiles and reaction times of human participants learning to solve a visuomotor learning task that is new for them. Overall, the architecture represents the first instance of a model bridging probabilistic planning and spiking-processes that has a degree of autonomy analogous to the one of real organisms.

Al-based memristor applied to habituation sensory nervous system

Sensory nervous system (SNS) can build the connections between organism and outside environment. Both of synapse and neuron are cornerstones of human biological system, which can transmit information to human brain and receive the feedback from central nervous system. Finally, the corresponding responses to the external information are performed. However, the information from outside environment should be received by SNS all the time. It is important for organism to distinguish between the stimuli that required attention and those that are irrelevant and no need to response. Habituation is one of fundamental properties of SNS to form such discrimination. It plays an important role for organism to adapt the environment and filter out irrelevantly repetitive information. In this study, an nc-Al/AlN structured based memristor with a thickness of 40 nm is produced by the sputtering method. The top and bottom electrode are of Ag and Al respectively, forming a sandwiched structure device. Habituation is found in the nc-Al/AlN thin film based memristor which has been rarely reported before. Both of current-voltage (<i>I</i>-<i>V</i> ) and pulse voltage measurement are executed on this device at room temperature. In the <i>I</i>-<i>V</i> measurement, the memristor shows unipolar switching properties which may be caused by conductive filament connecting or breaking. In the voltage pulse measurement, pulse interval is an important factor to affect memristor conduction. If the pulse interval is quite large, that is, the pulse frequency is low, the memristor will get maximized conduction very slow or in infinity time. If choosing an appropriate pulse voltage and interval value, the habituation will be observed after several stimulus pulses. The larger pulse interval needs more pulse numbers to cause memristor to be habituated, but which results in higher device conduction finally. A habituation memristor can act as synapse and connect with neuron to build the whole leaky integrate-and-fire (LIF) model which is quite often used in circuit design to mimic a real organism neuron behavior. In this model, neuron could be fired only when it gets enough stimuli from previous neuron. If the stimulus pulse frequency is low, there is observed no firing phenomenon in this case. In this study, the input signal of LIF model is a continuous voltage pulse with an amplitude of 1.2 V and interval of 5 ms. Such an input signal will be transmitted by habituation memristor to a neuron electronic element. The output signal is the pulse generated by neuron when it is fired. According to the results, the frequency of output signal is smaller than input information which complies with the basic characteristics of habituation. It is supposed that organisms should not response to this repetitive pulse any more and it will make neuron have more capabilities to handle following information.

Reconfigurable microbots folded from simple colloidal chains

To overcome the reversible nature of low-Reynolds-number flow, a variety of biomimetic microrobotic propulsion schemes and devices capable of rapid transport have been developed. However, these approaches have been typically optimized for a specific function or environment and do not have the flexibility that many real organisms exhibit to thrive in complex microenvironments. Here, inspired by adaptable microbes and using a combination of experiment and simulation, we demonstrate that one-dimensional colloidal chains can fold into geometrically complex morphologies, including helices, plectonemes, lassos, and coils, and translate via multiple mechanisms that can be varied with applied magnetic field. With chains of multiblock asymmetry, the propulsion mode can be switched from bulk to surface-enabled, mimicking the swimming of microorganisms such as flagella-rotating bacteria and tail-whipping sperm and the surface-enabled motion of arching and stretching inchworms and sidewinding snakes. We also demonstrate that reconfigurability enables navigation through three-dimensional and narrow channels simulating capillary blood vessels. Our results show that flexible microdevices based on simple chains can transform both shape and motility under varying magnetic fields, a capability we expect will be particularly beneficial in complex in vivo microenvironments.

Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data.

Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result, many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here, we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies (GWAS). We find that most common summary statistics have distributions that differ substantially from those seen in well-mixed populations, especially when Wright’s neighborhood size is < 100 and sampling is spatially clustered. “Stepping-stone” models reproduce some of these effects, but discretizing the landscape introduces artifacts that in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations revealed surprisingly little systematic bias. We also show that the combination of spatially autocorrelated environments and limited dispersal causes GWAS to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.

PIA: More Accurate Taxonomic Assignment of Metagenomic Data Demonstrated on sedaDNA From the North Sea

Assigning metagenomic reads to taxa presents significant challenges. Existing approaches address some issues, but are mostly limited to metabarcoding or optimised for microbial data. We present PIA (Phylogenetic Intersection Analysis): a taxonomic binner that works from standard BLAST output while mitigating key effects of incomplete databases. Benchmarking against MEGAN using sedaDNA suggests that, while PIA is less sensitive, it can be more accurate. We use known sequences to estimate the accuracy of PIA at up to 96% when the real organism is not represented in the database. For ancient DNA, where taxa of interest are frequently over-represented domesticates or absent, poorly-known organisms, more accurate assignment is critical, even at the expense of sensitivity. PIA offers an approach to objectively filter out false positive hits without the need to manually remove taxa and so make presuppositions about past environments and their palaeoecologies.

Miniaturizing landscapes to understand species distributions

Species’ geographic distributions shape global patterns of biodiversity and therefore have long been of interest to ecology and conservation. Theory has generated valuable hypotheses about how landscape structure, dispersal, biotic interactions and evolution shape range dynamics, but most predictions have not been tested on real organisms because key variables are difficult to isolate, replicate or manipulate in natural ecosystems. An exciting and rapidly emerging approach is to extend classical microcosm and mesocosm systems to create experimental ‘micro‐landscapes’. By enabling researchers to manipulate geographic features of interest, replicate landscapes, control colonization and follow dynamics across evolutionary timescales, micro‐landscapes allow explicit tests of the ecological and evolutionary underpinnings of species distributions. Here we review the micro‐landscape systems being used to advance biogeography, the major insights they have generated thus far, and the features that limit their application to some scenarios. We end by highlighting important questions about species’ biogeography that are ripe for testing with experimental micro‐landscapes, particularly those of immediate concern given rapid global change, such as range contractions and constraints to range expansion.