Tuning and Development of an Individual-Based Model of the Herring Spawning Migration

Norwegian spring spawning herring is a migratory pelagic fish stock that seasonally navigates between distant locations in the Norwegian Sea. The spawning migration takes place between late winter and early spring. In this article, we present an individual-based model that simulated the spawning migration, which was tuned and validated against observation data. Individuals were modelled on a continuous grid coupled to a physical oceanographic model. We explore the development of individual model states in relation to local environmental conditions and predict the distribution and abundance of individuals in the Norwegian Sea for selected years (2015–2020). Individuals moved position mainly according to the prevailing coastal current. A tuning procedure was used to minimize the deviations between model and survey estimates at specific time stamps. Furthermore, 4 separate scenarios were simulated to ascertain the sensitivity of the model to initial conditions. Subsequently, one scenario was evaluated and compared with catch data in 5 day periods within the model time frame. Agreement between model and catch data varies throughout the season and between years. Regardless, emergent properties of the migration are identifiable that match observations, particularly migration trajectories that run perpendicular to deep bathymetry and counter the prevailing current. The model developed is efficient to implement and can be extended to generate multiple realizations of the migration path. This model, in combination with various sources of fisheries-dependent data, can be applied to improve real-time estimates of fish distributions.

Low error estimation of half-cell thermodynamic parameters from whole-cell Li-ion battery experiments: Physics-based model formulation, experimental demonstration, and an open software tool

Low C-rate charge and discharge experiments, plus complementary differential voltage or differential capacity analysis, are among the most common battery characterization methods. Here, we adapt the multi-species, multi-reaction (MSMR) half-cell thermodynamic model to low C-rate cycling of whole-cell Li-ion batteries. MSMR models for the anode and cathode are coupled through whole-cell charge balances and cell-cycling voltage constraint equations, forming the basis for model-based estimation of MSMR half-cell parameters from whole-cell experimental data. Emergent properties of the whole-cell, like slippage of the anode and cathode lithiation windows, are also computed as cells cycle and degrade. A sequential least-square optimization scheme is used for parameter estimation from low-C cycling data of Samsung 18650 NMC|C cells. Low-error fits of the open-circuit cell voltage (e.g., under 5 mV mean absolute error for charge or discharge curves) and differential voltage curves for fresh and aged cells are achieved. We explore the features (and limitations) of using literature reference values for the MSMR half-cell thermodynamic parameters (reducing our whole-cell formulation to a 1-degree-of-freedom fit) and demonstrate the benefits of expanding the degrees of freedom by letting the MSMR parameters be tailored to the cell under test, within a constrained neighborhood of the half-cell reference values. Bootstrap analysis is performed on each dataset to show the robustness of our fitting to experimental noise and data sampling over the course of 600 cell cycles. The results show which specific MSMR insertion reactions are most responsible for capacity loss in each half-cell and the collective interactions that lead to whole-cell slippage and changes in useable capacity. Open-source software is made available to easily extend this model-based analysis to other labs and battery chemistries.

The Development of Single-Cell Metabolism and Its Role in Studying Cancer Emergent Properties

Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology––single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.

A network analysis of global cephalopod trade

The global trade in cephalopods is a multi-billion dollar business involving the fishing and production of more than ten commercially valuable species. It also contributes, in whole or in part, to the subsistence and economic livelihoods of thousands of coastal communities around the world. The importance of cephalopods as a major cultural, social, economic, and ecological resource has been widely recognised, but research efforts to describe the extent and scope of the global cephalopod trade are limited. So far, there are no specific regulatory and monitoring systems in place to analyse the traceability of the global trade in cephalopods at the international level. To understand who are the main global players in cephalopod seafood markets, this paper provides, for the first time, a global overview of the legal trade in cephalopods. Twenty years of records compiled in the UN COMTRADE database were analysed. The database contained 115,108 records for squid and cuttlefish and 71,659 records for octopus, including commodity flows between traders (territories or countries) weighted by monetary value (USD) and volume (kg). A theoretical network analysis was used to identify the emergent properties of this large trade network by analysing centrality measures that revealed key insights into the role of traders. The results illustrate that three countries (China, Spain, and Japan) led the majority of global market movements between 2000 and 2019. Based on volume and value, as well as the number of transactions, 11 groups of traders were identified. The leading cluster consisted of only eight traders, who dominated the cephalopod market in Asia (China, India, South Korea, Thailand, and Vietnam), Europe (the Netherlands, and Spain), and the USA. This paper identifies the countries and territories that acted as major importers or exporters, the best-connected traders, the hubs or accumulators, the modulators, the main flow routes, and the weak points of the global cephalopod trade network over the last 20 years. This knowledge of the network is crucial to move towards an environmentally sustainable, transparent, and food-secure global cephalopod trade.

Intelligent Cybernetic System Using Gestalt Processing

Purpose of the study. The aim of the study is to create and develop modern cyber physical systems. The evolution of cyber physical systems (CPS) is associated with the development of a cognitive approach within the framework of the application of mechanisms used by humans to solve their daily tasks. In the cognitive approach to working with cyber physical systems, gestalt is considered as one of the ways of solving modern tasks within the framework of the new Industry 4.0 technology. In the cognitive approach a simple task is considered for cyber physical systems of the Internet of Things (CPS IoT) with gestalt processing. When investigating such a task for a simple cyber physical system, it will be possible to use a gestalt with a simple structure. The complication of the task and structure of gestalt can occur with the development of CPS IoT. The article examines an intelligent cyber physical system of the Internet of Things using methods of gestalt processing of their states - a picture of the world, while solving various problems of the Internet of Things.Materials and research methods. To solve tasks within the framework of a cognitive approach to the construction and development of cyber physical systems, new methods and developments of specialists in the field of intelligent systems are required. In the context of Industry 4.0 technologies, the Internet of Things the gestalt processing of CPS IoT is considered. Within the framework of the cognitive approach sensory images, concept-representations, concept-scenarios, concept-gestalts of cyber physical systems are used to interact with the real world. It is important to use concept gestalts that can reflect CPS IoT with new emergent properties. CPS IoT gestalt refers to a certain state of the cyber physical system and its habitat, which occurs when a need arises and closes after the need is satisfied. The main task of gestalt processing for a cyber physical system is to satisfy its needs. The solution to this problem includes: the organization of the collection and the direct collection of the necessary elements for the formation of the gestalt, and later for its closure; the formation of the gestalt; the closure of the gestalt. For the accumulation of experience, its use and development, it is proposed to use machine learning methods. Machine learning results can be presented in the form of concept representations, concept scenarios.Results. The concepts-gestalts of CPS IoT, gestalt processing of CPS IoT are proposed within the framework of the cognitive approach. As the main stages of gestalt processing, the article highlights: - preparation of initial data for the formation of the need for CPS IoT: - formation of an imaginative perception - a picture of the world, including the current state of CPS IoT and necessary for the closure of the gestalt; - formation of gestalt; – formation of initial data for planning the control actions necessary for closing the CPS IoT gestalt; - implementation of control actions to close the CPS IoT gestalt; - saving the gestalt processing scenario for possible reuse in the future. These stages of gestalt processing relate to IoT CPS of any nature and are focused on any tasks of the Internet of Things. The demo example shows the use of gestalt processing for CPS IoT with a simple model without training.Conclusion. The article discusses the cognitive approach that refers to the use and development of intelligent cyber physical systems for the Internet of things and the Internet of everything. A method related to the gestalt processing of CPS IoT situations is proposed, which allows recognizing a need, and forming of a gestalt. Based on the generated CPS IoT gestalt, control actions are planned to close the CPS IoT gestalt. The implementation of the proposed approach, development and use of gestalt concepts will allow to reflect CPS IoT with new emergent properties.

Towards the First Principles in Biology and Cancer: New Vistas in Computational Systems Biology of Cancer

These days many leading scientists argue for a new paradigm for cancer research and propose a complex systems-view of cancer supported by empirical evidence. As an example, Thea Newman (2021) has applied “the lessons learned from physical systems to a critique of reductionism in medical research, with an emphasis on cancer”. It is the understanding of this author that the mesoscale constructs that combine the bottom-up as well as top-down approaches, are very close to the concept of emergence. The mesoscale constructs can be said to be those effective components through which the system allows itself to be understood. A short list of basic concepts related to life/biology fundamentals are first introduced to demonstrate a lack of emphasis on these matters in literature. It is imperative that physical and chemical approaches are introduced and incorporated in biology to make it more conceptually sound, quantitative, and based on the first principles. Non-equilibrium thermodynamics is the only tool currently available for making progress in this direction. A brief outline of systems biology, the discovery of emergent properties, and metabolic modeling are introduced in the second part. Then, different cancer initiation concepts are reviewed, followed by application of non-equilibrium thermodynamics in the metabolic and genomic analysis of initiation and development of cancer, stressing the endogenous network hypothesis (ENH). Finally, extension of the ENH is suggested to include a cancer niche (exogenous network hypothesis). It is expected that this will lead to a unifying systems–biology approach for a future combination of the analytical and synthetic arms of two major hypotheses of cancer models (SMT and TOFT).

Emergent gauge symmetries: making symmetry as well as breaking it

Gauge symmetries play an essential role in determining the interactions of particle physics. Where do they come from? Might the gauge symmetries of the Standard Model unify in the ultraviolet or might they be emergent in the infrared, below some large scale close to the Planck scale? Emergent gauge symmetries are important in quantum many-body systems in quantum phases associated with long range entanglement and topological order, e.g. they arise in high temperature superconductors, with string-net condensation and in the A-phase of superfluid 3 He. String-nets and superfluid 3 He exhibit emergent properties similar to the building blocks of particle physics. Emergent gauge symmetries also play an important role in simulations of quantum field theories. This article discusses recent thinking on possible emergent gauge symmetries in particle physics, commenting also on Higgs phenomena and the vacuum energy or cosmological constant puzzle in emergent gauge systems. This article is part of the theme issue ‘Quantum technologies in particle physics’.

Genetic variation in haemoglobin is associated with evolved changes in breathing in high-altitude deer mice

Physiological systems often have emergent properties but the effects of genetic variation on physiology are often unknown, which presents a major challenge to understanding the mechanisms of phenotypic evolution. We investigated whether genetic variants in haemoglobin (Hb) that contribute to high-altitude adaptation in deer mice (Peromyscus maniculatus) are associated with evolved changes in control of breathing. We created F2 inter-population hybrids of highland and lowland deer mice to test for phenotypic associations of α- and β-globin variants on a mixed genetic background. Hb genotype had expected effects on Hb-O2 affinity that were associated with differences in arterial O2 saturation in hypoxia. However, high-altitude genotypes were also associated with breathing phenotypes that should contribute to enhancing O2 uptake in hypoxia. Mice with highland α-globin exhibited a more effective breathing pattern, with highland homozygotes breathing deeper but less frequently across a range of inspired O2, and this difference was comparable to the evolved changes in breathing pattern in deer mouse populations native to high altitude. The ventilatory response to hypoxia was augmented in mice that were homozygous for highland β-globin. The association of globin variants with variation in breathing phenotypes could not be recapitulated by acute manipulations of Hb-O2 affinity, because treatment with efaproxiral (a synthetic drug that acutely reduces Hb-O2 affinity) had no effect on breathing in normoxia or hypoxia. Therefore, adaptive variation in haemoglobin may have unexpected effects on physiology in addition to the canonical function of this protein in circulatory O2 transport.

Nested calcium dynamics support daily cell unity and diversity in the suprachiasmatic nuclei of free-behaving mice

The suprachiasmatic nuclei (SCN) of the anterior hypothalamus host the circadian pacemaker that synchronizes mammalian rhythms with the day-night cycle. SCN neurons are intrinsically rhythmic, thanks to a conserved cell-autonomous clock mechanism. In addition, circuit-level emergent properties confer a unique degree of precision and robustness to SCN neuronal rhythmicity. However, the multicellular functional organization of the SCN is not yet fully understood. Although SCN neurons are well coordinated, experimental evidences indicate that some neurons oscillate out of phase in SCN explants, and possibly to a larger extent in vivo. Here, we used microendoscopic Ca2+i imaging to investigate SCN rhythmicity at a single cell resolution in free-behaving mice. We found that SCN neurons in vivo exhibited fast Ca2+i spikes superimposed upon slow changes in baseline Ca2+i levels. Both spikes and baseline followed a time-of-day modulation in many neurons, but independently from each other. Daily rhythms in basal Ca2+i were well coordinated, while spike activity from the same neurons peaked at multiple times of the light cycle, and unveiled clock-independent interactions at the multicellular level. Hence, fast Ca2+i spikes and slow changes in baseline Ca2+i levels highlighted how diverse activity patterns could articulate within the temporal network unity of the SCN in vivo, and provided support for a multiplex neuronal code in the circadian pacemaker.