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Background: Circadian rhythms are important for all aspects of biology; virtually every aspect of biological function varies according to time of day. Although this is well known, variation across the day is also often ignored in the design and reporting of research. For this review, we analyzed the top 50 cited papers across 10 major domains of the biological sciences in the calendar year 2015. We repeated this analysis for the year 2019, hypothesizing that the awarding of a Nobel Prize in 2017 for achievements in the field of circadian biology would highlight the importance of circadian rhythms for scientists across many disciplines, and improve time-of-day reporting. Results: Our analyses of these 1000 empirical papers, however, revealed that most failed to include sufficient temporal details when describing experimental methods and that few systematic differences in time-of-day reporting existed between 2015 and 2019. Overall, only 6.1% of reports included time-of-day information about experimental measures and manipulations sufficient to permit replication. Conclusions: Circadian rhythms are a defining feature of biological systems, and knowing when in the circadian day these systems are evaluated is fundamentally important information. Failing to account for time of day hampers reproducibility across laboratories, complicates interpretation of results, and reduces the value of data based predominantly on nocturnal animals when extrapolating to diurnal humans. [ABSTRACT FROM AUTHOR]

The concept of regulation pervades biology, for example in models of genetic regulatory networks and the endocrine system. Regulation has a normative opposite, dysregulation, which figures prominently in biomedical models of disease. The use of normative concepts in biology, however, has been thought to present some challenges for the physicalist view of the world, and various resolutions have been proposed. Up to now the problem of biological normativity has been debated largely in connection with the concept of biological information. In this paper we shift focus to the concept of biological regulation, proposing that it provides a promising new approach to these issues. Models of regulatory systems have several features: they are causal, but they do not deal with the energy exchanges and transformations covered by physics and chemistry; further, and entirely connected, regulatory systems can break down, and this is because they and their causal-regulatory properties are dependent on fragile molecular structures. Biological regulatory systems exhibit normativity, because they are not determined by physical and chemical laws, but their close relationship with physical laws and physicalist ontology is transparent. [ABSTRACT FROM AUTHOR]

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon's, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, this paper explores the aforementioned disconnect between classical information theory and biology. In this paper, we present a model, specified as a computational state machine, which is then utilized in a simulation framework constructed specifically to reveal emergence of a "subjective information", i.e., trade-off between a living system's capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time. Simulations clearly show that a strategy that maximizes information efficiency results in a lower growth rate with respect to the strategy that gains less information but contains a higher meaning for survival. [ABSTRACT FROM AUTHOR]

It is a matter of contention whether or not a general explanatory framework for the biological sciences would be of scientific value, or whether it is even achievable. In this paper we suggest that both are the case, and we outline proposals for a framework capable of generating new scientific questions. Starting with one clear characteristic of biological systems—that they all have the potential to make mistakes—we aim to describe the nature of this potential and the common processes that lie behind it. Given that under most circumstances biological systems function effectively, an examination of different kinds of mistake-making provides pointers to mechanisms that must exist to make failure uncommon. This, in turn, informs a framework for systematic inquiry, which in this paper we apply to the hemostatic system, but we believe could be applied to any system across biology. [ABSTRACT FROM AUTHOR]

Two hallmarks of biological processes are complexity and time. While complexity can have many meanings, in this paper we propose an explicit link to the flow of time and how it is experienced by the organism. While the flow of time is rooted in constraints of fundamental physics, understanding the operation of biological systems in terms of processual flow and tempo is more elusive. Fortunately, the convergence of new computational and methodological perspectives will provide a means to transform complicated, nonlinear paths between related phenomena at different time scales into dynamic four-dimensional perspectives. According to the complex temporal biology approach, information flow between time scales of multiple lengths is a transformational process that acts to regulate life's complexity. Interactions between temporal intervals of differing magnitude and otherwise loosely-related mechanisms can be understood as inter-timescale information flow. We further propose that informational flow between time scales is the glue that binds the multiple vertical layers of biocomplexity, as well as yielding surprising outcomes ranging from complex behaviors to the persistence of lineages. Building a foundation of rules based on common interactions between orders of time and common experiential contexts would help to reintegrate biology. Emerging methodologies such as state-of-the-art imaging, visualization techniques, and computational data analysis can help us uncover these interactions. In conclusion, we propose educational and community-level changes that would better enable our vision. [ABSTRACT FROM AUTHOR]

Both mathematical models and biological model systems stand as tractable representations of complex biological systems or behaviors. They facilitate research and provide insights, and they can describe general rules. Models that represent biological processes or formalize general hypotheses are essential to any broad understanding. Mathematical or biological models necessarily omit details of the natural systems and thus may ultimately be "incorrect" representations. A key challenge is that tractability requires relatively simple models but simplification can result in models that are incorrect in their qualitative, broad implications if the abstracted details matter. Our paper discusses this tension, and how we can improve our inferences from models. We advocate for further efforts dedicated to model development, improvement, and acceptance by the scientific community, all of which may necessitate a more explicit discussion of the purpose and power of models. We argue that models should play a central role in reintegrating biology as a way to test our integrated understanding of how molecules, cells, organs, organisms, populations, and ecosystems function. [ABSTRACT FROM AUTHOR]

Recent years have seen a rise in research where so called "digital biomarkers" represent the focal study interest. Many researchers understand that digital biomarkers describe digital footprints providing insights into healthy and pathological human (neuro-)biology. Beyond that the term digital biomarker is also used at times to describe more general concepts such as linking digital footprints to human behavior (which itself can be described as the result of a biological system). Given the lack of consensus on how to define a digital biomarker, the present short mini-review provides i) an overview on various definitions and ii) distinguishes between direct (narrow) or indirect (broad) concepts of digital biomarkers. From our perspective, digital biomarkers meant as a more direct (or narrow) concept describe digital footprints being directly linked to biological variables, such as stemming from molecular genetics, epigenetics, endocrinology, immunology or brain imaging, to name a few. More indirect concepts of digital biomarkers encompass digital footprints being linked to human behavior that may act as latent variables indirectly linked to biological variables. [ABSTRACT FROM AUTHOR]

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Fractals with different levels of self-similarity and magnification are defined as reduced fractals. It is shown that spectra of these reduced fractals can be constructed and used to describe levels of complexity of natural phenomena. Specific applications to biological systems, such as green algae, are performed, and it is suggested that the obtained spectra can be used to classify the considered algae by identifying spectra associated with them. The ranges of these spectra for green algae are determined and their extension to other biological as well as other natural systems is proposed. [ABSTRACT FROM AUTHOR]

Ervin Bauer (1890–1938) outlined the paradigm of theoretical biology from the perspective of biophysics and bioenergetics. His molecular-based biological theory is centered on the principle of sustainable non-equilibrium, which is continuously produced and maintained by all biological systems throughout their life. Ervin Bauer became the victim of Stalin's Great Terror. Here we present two of the fundamental works of Ervin Bauer in English translation: the paper "The definition of living beings on the basis of their thermodynamic properties, and the fundamental biological principles that follow from it" published in Naturwissenschaften (1920) and the excerpts from his magnum opus "Theoretical Biology" (1935). These works became a bibliographical rarity. A complete English translation of "Theoretical Biology" is an important task for the future. [ABSTRACT FROM AUTHOR]

We introduce biology to the artist's design tool, the storyboard. This versatile organizing and visualizing artistic platform is introduced into the biology classroom to aid in an inventive and focused discovery process. Almost all biological concepts are dynamic, and storyboards offer biology, lecture, wet and computational labs, flexibility, inventiveness, and an opportunity for students to slow down the so-called steps of biological processes and moderate their observations. Storyboarding is a thoughtful and reflective discovery device with enormous potential to break with traditional biology classroom experiences and return to the root of the educational process: storytelling. It will encourage teachers to embark on the remodeling of the biological curriculum with specific technical skills that students and teachers should consider developing to make the STEAM experience tailored to the uniqueness of biological systems. Storyboards offer hands-on, illustrative, and interactive conversations about biology concepts. They are an "unplugged" and contemplative experiences, organizing frameworks for personal expression focused on biological wonders. [ABSTRACT FROM AUTHOR]

This paper distinguishes two ways in which Kant's ideas concerning the relation between teleology and biological organization have been taken up in contemporary philosophy of biology and theoretical biology. The first sees his account as the first instance of the modern understanding of teleology as a heuristic tool aimed at producing mechanistic explanations of organismal form and function. The second sees in Kant's concept of intrinsic purposiveness the seed of a radically new way of thinking about biological systems that should be developed by turning teleology into a legitimate concept of natural science. We name the two approaches heuristic and naturalistic , respectively. Our aim is to critically evaluate these approaches and suggest that the naturalistic option, which remains a minority position, deserves to be taken more seriously than it currently is in contemporary biological theory. While evolution by natural selection closes the case on intelligent design, it does not close the case on teleology in general. In fact, the current return of the organism and the recent calls for an agential perspective in evolutionary biology point out that we still have some thinking to do concerning this side of Kant's legacy. • We address the legacy of Kant's account of teleology for philosophy of biology. • We argue that this legacy implies two contrasting approaches: heuristic and naturalistic. • We critically evaluate these two approaches. • We argue for the plausibility of a non-reductionist, yet naturalistic account of teleology. [ABSTRACT FROM AUTHOR]

The central aim of this Special Issue, devoted to a multifocal study of the geometry and phenomenology of the living, is to show the need of working with models that integrate geometrical and topological objects and operations, dynamical variables and specific biological mechanisms and their relationships with one another. A multilevel and integrative approach has essentially to consider the fact that simply knowing the parts list of genes and proteins does not tell us much about how life's many biological processes works. The cellular organization is a complex dynamical system with hundreds of thousands of bio-molecules interacting with one another to execute organism's many related functions. It is argued that the production of complex living organisms owes much of its working to some topological mechanisms (i.e. four-dimensional transformations and deformations) which operate markedly on the three levels of the organization, regulation and evolution of biological systems. Thus, one can speak of a specific topology of the living acting very dynamically on the (non-fixed) substrate space of the physiological and metabolic activities of all complex living organisms. Philosophically, it is now clear that the genetic causality theory has several limitations, both intrinsic because of the multilevel complexity of biological processes, and extrinsic in that it disregards the influence of the phenotype on the genotype and in particular the possibility that certain acquired characteristics can be inherited. In a sense, we can say that the molecular biological conception of recent decades has limited or even misleadingly impacted our vision of the living world. New ideas are needed if we are to succeed in unravelling multifactorial genetic, epigenetic and environmental causation at higher levels of physiological function and so to explain fundamental living phenomena that genetics alone is unable to explain. Some of the most significant conclusions proposed in this issue are that (i) structural plasticity and biological functionality are deeply related, (ii) the biological information and signification is inherently spatial and temporal, it is not unidirectional, and it essentially evolve following a complex and changing network-like organization, (iii) finally, gene ontology is incomplete and it as to be supplied by integrating other fundamental levels of the organization and regulation of the living systems. [ABSTRACT FROM AUTHOR]

Ervin Bauer (1890–1938) was the first to build a general molecular-based biological theory. He defined the basic principles of theoretical biology from a thermodynamic perspective, focusing on the capacity of biological systems to produce and support the state of sustainable non-equilibrium. His central work "Theoretical Biology" (1935) was written long before modern advances in molecular biology, genetics, and information theory. Ervin Bauer and his wife Stefánia were executed in Stalin's Great Terror. This paper presents a brief introduction to Ervin Bauer's life and includes his short biography. [ABSTRACT FROM AUTHOR]

It is a great pleasure to be part of the celebration of Nitant Kenkre's scientific career. In this paper, we review mathematical models for studying evolutionary branching and diversification in biology. We highlight insights provided by Nitant Kenkre's work in the early 2000s as part of the Consortium of the Americas for Interdisciplinary Science. We focus on the use of these models for understanding the conditions that promote phenotypic diversification and innovation in biological systems, understood as the branching of stationary solutions, and some of the ideas that emerged as part of the research Consortium. We highlight recent developments and applications of stochastic, nonlocal partial derivative formalisms. [ABSTRACT FROM AUTHOR]

A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering. A Perspective on how the tools of behavioral science and the emerging field of diverse intelligence help to understand decision-making of cellular collectives in evolutionary and biomedical contexts. [ABSTRACT FROM AUTHOR]

Recent advancements in single‐cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single‐cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology. [ABSTRACT FROM AUTHOR]

Designing effective curricula is challenging. Content decisions can impact both learning outcomes and student engagement. As an example consider the place of Hardy-Weinberg equilibria (HWE) and genetic drift calculations in introductory biology courses, as discussed by Masel (2012). Given that population genetics, "a fairly arcane speciality", can be difficult to grasp, there is little justification for introducing introductory students to HWE calculations. It is more useful to introduce them to the behavior of alleles in terms of basic features of biological systems, and that in the absence of selection recessive alleles are no "weaker" or preferentially lost from a population than are dominant alleles. On the other hand, stochastic behaviors, such as genetic drift, are ubiquitous in biological systems and often play functionally significant roles; they can be introduced to introductory students in mechanistic and probabilistic terms. Specifically, genetic drift emerges from the stochastic processes involved in meiotic chromosome segregation and recombination. A focus on stochastic processes may help counteract naive bio-deterministic thinking and can reinforce, for students, the value of thinking quantitatively about biological processes. [ABSTRACT FROM AUTHOR]

Biomimicry is a powerful science that takes inspiration from nature's innovative solutions to challenging problems. In this work, we use asymptotic methods to develop the mathematical foundations for the exchange of design inspiration and features between biological hearing systems, signal processing algorithms, and acoustic metamaterials. Our starting point is a concise asymptotic analysis of high-contrast acoustic metamaterials. We are able to fine tune this graded structure to mimic the biomechanical properties of the cochlea, at the same scale. We then turn our attention to developing a biomimetic signal processing algorithm. We use the response of the cochlea-like metamaterial as an initial filtering layer and then add additional biomimetic processing stages, designed to mimic the human auditory system's ability to recognize the global properties of natural sounds. This demonstrates the three-way exchange of ideas that, thanks to our analysis, is possible between signal processing, metamaterials and biology. [ABSTRACT FROM AUTHOR]

The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence—reinforcement learning—to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty. Author summary: Biological systems are often complex and typically exhibit non-linear behaviour, making accurate model parametrisation difficult. Optimal experimental design tools help address this problem by identifying experiments that are predicted to provide maximally informative data for parameter inference. In this work we use reinforcement learning, an artificial intelligence method, to determine such experiments. Our simulation studies show that this approach allows uncertainty in model parameterisation to be directly incorporated into the search for optimal experiments, opening a practical avenue for training an experimental controller. We present this method as complementary to existing optimisation approaches and we anticipate that artificial intelligence has a valuable role to play in the future of optimal experimental design. [ABSTRACT FROM AUTHOR]

Adaptation is a running theme in biology. It allows a living system to survive and thrive in the face of unpredictable environments by maintaining key physiological variables at their desired levels through tight regulation. When one such variable is maintained at a certain value at the steady state despite perturbations to a single input, this property is called robust perfect adaptation (RPA). Here we address and solve the fundamental problem of maximal RPA (maxRPA), whereby, for a designated output variable, RPA is achieved with respect to perturbations in virtually all network parameters. In particular, we show that themaxRPA property imposes certain structural constraints on the network. We then prove that these constraints are fully characterized by simple linear algebraic stoichiometric conditions which differ between deterministic and stochastic descriptions of the dynamics. We use our results to derive a new internal model principle (IMP) for biomolecular maxRPA networks, akin to the celebrated IMP in control theory. We exemplify our results through several known biological examples of robustly adapting networks and construct examples of such networks with the aid of our linear algebraic characterization. Our results reveal the universal requirements for maxRPA in all biological systems, and establish a foundation for studying adaptation in general biomolecular networks, with important implications for both systems and synthetic biology. [ABSTRACT FROM AUTHOR]

Since spiking neural networks (SNNs) can effectively simulate the information processing mechanism of the biological cortex, they are expected to bridge the gap between neuroscience and machine learning. The hardware simulation of large-scale SNNs requires a simple and versatile silicon neuron model framework. In this article, a spiking neuron circuit as the core device of SNNs is presented. The proposed neuron circuit can mimic the dynamics of different types of biological neurons by adjusting the bias voltage. In order to facilitate the implementation of the spiking neuron circuit based on complementary metal-oxide-semiconductor (CMOS) and reduce the overhead of the circuit area, a modified Mihalas–Niebur (MN) mathematical model is adopted. The improved MN model is biologically plausible and can still successfully display all dynamic behaviors observed in biology. The function of the proposed neuron circuit has been verified by the phase diagram analysis method. The simulation results show the designed neuron circuit can successfully replicate 15 of the 20 firing patterns exhibited by the biological cortex, which proves that the neuron can act as a universal spiking neuron in very large-scale integrated circuit (VLSI) neuromorphic networks. [ABSTRACT FROM AUTHOR]

With the advances in genomic sequencing, many organisms with novel biological properties are ripe for use as emerging model organisms. However, to make full use of them, transformation methods need to be developed to permit genome editing. Here, we present the development of transformation for the fungus fly Bradysia (Sciara) coprophila; this may serve as a paradigm for the development of transformation for other emerging systems, especially insects. Bradysia (Sciara) has a variety of unique biological features, including locus-specific developmentally regulated DNA amplification, chromosome imprinting, a monopolar spindle in male meiosis I, non-disjunction of the X chromosome in male meiosis II, X chromosome elimination in early embryogenesis, germ-line-limited (L) chromosomes and high resistance to radiation. Mining the unique biology of Bradysia (Sciara) requires a transformation system to test mutations of DNA sequences that may play roles for these features. We describe a Bradysia (Sciara) transformation system using a modified piggyBac transformation vector and detailed protocols we have developed to accommodate Bradysia (Sciara) specific requirements. This advance will provide a platform for us and others in the growing Bradysia (Sciara) community to take advantage of this unique biological system. In addition, the versatile piggyBac vectors described here and transformation methods will be useful for other emerging model systems. [ABSTRACT FROM AUTHOR]

Introduction: Individualized response to the immune triggers influences the course of immune-mediated diseases and the response to immunotherapies. Both inter- and intra-subject variations occur in time-dependent dynamics of biological systems. The present study aimed to establish a model for inherent personalized-time-dependent variability in response to immune triggers. Methods: Male C57BL/6 mice were administered concanavalin A (ConA) and followed every 2 h for 10 h and at 24 h for serum alanine aminotransferase (ALT) levels. Results: A marked intragroup variability was noted for both the timing of the effect of ConA, the magnitude of the increase in ALT levels, and the time to peak. While in some mice, a peak level was achieved, whereas a continuous increase in liver damage was noted in others. Four mice died at different time points during the study irrespective of their liver damage, further supporting the individualized-based response to the trigger. Conclusions: This feasibility study established a model for determining the personalized-inherent variability in a time-dependent response to the immune triggers. These results highlight the importance of considering both the time and the wide range of individualized variability in immune responses while designing personalized-based immunotherapies. [ABSTRACT FROM AUTHOR]

Information, energy, and matter are fundamental properties of all levels of biological organization, and life emerges from the continuous flux of matter, energy, and information. This perspective piece defines and explains each of the three pillars of this nexus. We propose that a quantitative characterization of the complex interconversions between matter, energy, and information that comprise this nexus will help us derive biological insights that connect phenomena across different levels of biological organization. We articulate examples from multiple biological scales that highlight how this nexus approach leads to a more complete understanding of the biological system. Metrics of energy, information, and matter can provide a common currency that helps link phenomena across levels of biological organization. The propagation of energy and information through levels of biological organization can result in emergent properties and system-wide changes that impact other hierarchical levels. Deeper consideration of measured imbalances in energy, information, and matter can help researchers identify key factors that influence system function at one scale, highlighting avenues to link phenomena across levels of biological organization and develop predictive models of biological systems. [ABSTRACT FROM AUTHOR]

During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. We propose a new, data-driven approach that harnesses our knowledge of the functioning of biological systems to expand current conceptual frameworks and develop corresponding models that can more accurately represent and predict future eco-evolutionary outcomes. We suggest a roadmap toward achieving this goal. This long-term vision will move biology in a direction that can wield these predictive models for scientific applications that benefit humanity and increase the resilience of natural biological systems. We identify short, medium, and long-term key objectives to connect our current state of knowledge to this long-term vision, iteratively progressing across three stages: (1) utilizing knowledge of biological systems to better inform eco-evolutionary models, (2) generating models with more accurate predictions, and (3) applying predictive models to benefit the biosphere. Within each stage, we outline avenues of investigation and scientific applications related to the timescales over which evolution occurs, the parameter space of eco-evolutionary processes, and the dynamic interactions between these mechanisms. The ability to accurately model, monitor, and anticipate eco-evolutionary changes would be transformational to humanity's interaction with the global environment, providing novel tools to benefit human health, protect the natural world, and manage our planet's biosphere. [ABSTRACT FROM AUTHOR]

Forecasting how climate change will impact biological systems represents a grand challenge for biologists. However, climate change biology lacks an effective framework for anticipating and resolving uncertainty. Here, we introduce the concept of climate change wildcards: biological or bioclimatic processes with a high degree of uncertainty and a large impact on our ability to address the biotic consequences of climate change. Wildcards may occur at multiple points in the progression of research—from understanding, to predicting, to forecasting biological responses. Our understanding of biological responses is limited by the components and processes we exclude to make research tractable. Our ability to predict biological responses often requires integration between biological levels of organization, across multiple stressors, and from specific cases to general systems. However, these types of integration can be dramatically affected by, respectively, differences between biological levels in their critical points, nonadditivity of the effects of different stressors, and historical and geographic contingency. Finally, our ability to forecast biological responses to climate change requires incorporating climatic projections in bioclimatic models. Such forecasts are vulnerable to the compounding of biological and climatic uncertainty, especially when biological responses occur in novel areas of bioclimatic parameter space. Both biological responses and climate change are dynamic processes; the potential of biological systems to be buffered against or rescued from the effects of climate change depends on the relative timing of biological and climatic effects—one of the least predictable aspects of both systems. In sum, our framework identifies stress points in the research process where we should anticipate and forestall wildcards. Focusing on universal currencies, like energy and elements, and universal structures, like functional traits and ecological networks, will improve our ability to generalize results. Most importantly, by modeling and communicating uncertainty, climate change biology can identify critical foci for future research. [ABSTRACT FROM AUTHOR]

Nanotechnology provides the tools and technology platforms for the investigation and transformation of biological systems, and biology offers inspiration models and bio-assembled components to nanotechnology. The conference was organized in the following topics: Engineered nanomaterials, Nanomaterials in medicine and therapeutics, Nanosystems for 3D models and tissue engineering, Nanoimaging for diagnosis and detection, Biological nanodevices and biosensors, Bioinspired and biomimetic materials, Nanomaterials in agriculture, Nanotechnology in gene delivery, Nanomaterials and environmental effects, Biological uptake and Nanotoxicology. Nanomaterials (nanoparticles, nanowires, nanofibers, nanotubes) have opened up new dimensions in science and technology and created new opportunities in many biological applications (biosensing, biological separation, molecular imaging, anticancer therapy). [Extracted from the article]

Neuromorphic computing is becoming a popular approach for implementations of brain-inspired machine learning tasks. As a paradigm for both hardware and algorithm design, neuromorphic computing aims to emulate several aspects related to the structure and function of the biological nervous system to achieve artificial intelligence with efficiencies that are orders of magnitude better than those exhibited by general-purpose computing hardware. We provide a holistic treatment of spike-based neuromorphic computing (i.e., based on spiking neural networks), detailing biological motivation, key aspects of neuromorphic algorithms, and a survey of state-of-the-art neuromorphic hardware. In particular, we focus on these aspects within the context of brain-inspired vision applications. Our aim is to serve as a complement to several of the existing reviews on neuromorphic computing while also providing a unique perspective. [ABSTRACT FROM AUTHOR]