Intelligence is a human construct to represent the ability to achieve goals. Given this wide berth, intelligence has been defined countless times, studied in a variety of ways and represented using numerous indicators. Understanding intelligence ultimately requires theory and quantification, both of which have proved elusive. I present an Information Framework of Intelligence (IFI) that applies across all systems from physics to biology, humans and AI. Central to this framework is the "intelligence niche", which provides a conceptual basis for understanding constraints on intelligence and the evolution of intelligence. IFI likens intelligence to a real-time calculus, differentiating, correlating and integrating information, and anticipating or predicting future contingencies. I propose a classification of the levels and scales at which intelligence operates. Focussing on measurable macrosopic phenomena, I develop quantitative indicators of intelligence based on abilities to identify and resolve subgoals. Intelligence is reflected by acquired goal-useful information relative to goal complexity, or to the related concept of goal difficulty, or to an arbitrary reference such as a benchmark. I conclude with predictions and implications of IFI.

Multicellular organisms are confronted not only with mutation in germline, but also mutations emerging in somatic cells. Somatic mutations can lead cancers and possibly contribute to aging phenotypes. Prevailing wisdom suggests somatic mutations are limited by evolved defences, either targeting DNA or post-mutational cellular states. Here, I analyse simple models for metazoans and humans in particular, incorporating the possibility that mutation rate trades off of organism longevity, defined as the age at which natural selection becomes negligible. The models assume selection acts to reduce mutation rate and prevent disease, both of which contribute to selecting for longer lifespan. We detail equilibrium conditions and find coevolutionary oscillations under certain parameter combinations. Notably, disease prevention can lead to mutational tolerance and this can contribute to explaining empirical cross-species patterns in longevity vs. mutation rate. We conclude that mutational disease and cancer in particular have required characteristics to be mediators of mutation rate-lifespan coevolution. The mutational tolerance that results from this coevolution can contribute to explaining the incidences of other diseases and conditions, including aging. ### Competing Interest Statement The authors have declared no competing interest.

Antimicrobial resistance (AMR) is a major global health issue. Current measures for tackling it comprise mainly the prudent use of drugs, the development of new drugs, and rapid diagnostics. Relatively little attention has been given to forecasting the evolution of resistance. Here, we argue that forecasting has the potential to be a great asset in our arsenal of measures to tackle AMR. We argue that, if successfully implemented, forecasting resistance will help to resolve the antibiotic crisis in three ways: it will (i) guide a more sustainable use (and therefore lifespan) of antibiotics and incentivize investment in drug development, (ii) reduce the spread of AMR genes and pathogenic microbes in the environment and between patients, and (iii) allow more efficient treatment of persistent infections, reducing the continued evolution of resistance. We identify two important challenges that need to be addressed for the successful establishment of forecasting: (i) the development of bespoke technology that allows stakeholders to empirically assess the risks of resistance evolving during the process of drug development and therapeutic/preventive use, and (ii) the transformative shift in mindset from the current praxis of mostly addressing the problem of antibiotic resistance a posteriori to a concept of a priori estimating, and acting on, the risks of resistance.

Intelligence is a human construct to represent the ability to achieve goals. Given this wide berth, intelligence has been defined countless times, studied in a variety of ways and represented using numerous measures. Understanding intelligence ultimately requires theory and quantification, both of which have proved elusive. I develop a framework – the Theory of Intelligences (TIS) – that applies across all systems from physics, to biology, humans and AI. TIS likens intelligence to a calculus, differentiating, correlating and integrating information. Intelligence operates at many levels and scales and TIS distils these into a parsimonious macroscopic framework centered on solving, planning and their optimization to accomplish goals. Notably, intelligence can be expressed in informational units or in units relative to goal difficulty, the latter defined as complexity relative to system (individual or benchmarked) ability. I present general equations for intelligence and its components, and a simple expression for the evolution of intelligence traits. The measures developed here could serve to gauge different facets of intelligence for any step-wise transformation of information. I argue that proxies such as environment, technology, society and collectives are essential to a general theory of intelligence and to possible evolutionary transitions in intelligence, particularly in humans. I conclude with testable predictions of TIS and offer several speculations.

COPYRIGHT © 2023 Hochberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Frontiers in Science Viewpoint PUBLISHED 28 February 2023 DOI 10.3389/fsci.2023.1123806

Metastasis is a nonrandom process with varying degrees of organotropism-specific source-acceptor seeding. Understanding how patterns between source and acceptor tumors emerge remains a challenge in oncology. We hypothesize that organotropism results from the macronutrient niche of cells in source and acceptor organs. To test this, we constructed and analyzed a metastatic network based on 9303 records across 28 tissue types. We found that the topology of the network is nested and modular with scale-free degree distributions, reflecting organotropism along a specificity/generality continuum. The variation in topology is significantly explained by the matching of metastatic cells to their stoichiometric niche. Specifically, successful metastases are associated with higher phosphorus content in the acceptor compared to the source organ, due to metabolic constraints in proliferation crucial to the invasion of new tissues. We conclude that metastases are codetermined by processes at source and acceptor organs, where phosphorus content is a limiting factor orchestrating tumor ecology.

Intelligence is a human construct to represent the ability to achieve goals. Given this wide berth, intelligence has been defined countless times, studied in a variety of ways and quantified using numerous measures. Understanding intelligence ultimately requires theory and quantification, both of which are elusive. My main objectives are to identify some of the central elements in and surrounding intelligence, discuss some of its challenges and propose a theory based on first principles. I focus on intelligence as defined by and for humans, frequently in comparison to machines, with the intention of setting the stage for more general characterizations in life, collectives, human designs such as AI and in non-designed physical and chemical systems. I discuss key features of intelligence, including path efficiency and goal accuracy, intelligence as a Black Box, environmental influences, flexibility to deal with surprisal, the regress of intelligence, the relativistic nature of intelligence and difficulty, and temporal changes in intelligence including its evolution. I present a framework for a first principles Theory of IntelligenceS (TIS), based on the quantifiable macro-scale system features of difficulty, surprisal and goal resolution accuracy. The proposed partitioning of uncertainty/solving and accuracy/understanding is particularly novel since it predicts that paths to a goal not only function to accurately achieve goals, but as experimentations leading to higher probabilities for future attainable goals and increased breadth to enter new goal spaces. TIS can therefore explain endeavors that do not necessarily affect Darwinian fitness, such as leisure, politics, games and art. I conclude with several conceptual advances of TIS including a compact mathematical form of surprisal and difficulty, the theoretical basis of TIS, and open questions.

Bacteria release and sense small molecules called autoinducers in a process known as quorum sensing. The prevailing interpretation of quorum sensing is that by sensing autoinducer concentrations, bacteria estimate population density to regulate the expression of functions that are only beneficial when carried out by a sufficiently large number of cells. However, a major challenge to this interpretation is that the concentration of autoinducers strongly depends on the environment, often rendering autoinducer-based estimates of cell density unreliable. Here we propose an alternative interpretation of quorum sensing, where bacteria, by releasing and sensing autoinducers, harness social interactions to sense the environment as a collective. Using a computational model we show that this functionality can explain the evolution of quorum sensing and arises from individuals improving their estimation accuracy by pooling many imperfect estimates - analogous to the 'wisdom of the crowds' in decision theory. Importantly, our model reconciles the observed dependence of quorum sensing on both population density and the environment and explains why several quorum sensing systems regulate the production of private goods.

Aging research is unparalleled in the breadth of disciplines it encompasses, from evolutionary studies examining the forces that shape aging to molecular studies uncovering the underlying mechanisms of age-related functional decline. Despite a common focus to advance our understanding of aging, these disciplines have proceeded along distinct paths with little cross-talk. We propose that the concept of resilience can bridge this gap. Resilience describes the ability of a system to respond to perturbations by returning to its original state. Although resilience has been applied in a few individual disciplines in aging research such as frailty and cognitive decline, it has not been explored as a unifying conceptual framework that is able to connect distinct research fields. We argue that because a resilience-based framework can cross broad physiological levels and time scales it can provide the missing links that connect these diverse disciplines. The resulting framework will facilitate predictive modeling and validation and influence targets and directions in research on the biology of aging.

A recommendation of: Alexis Bénard, Hélène Henri, Camille Noûs, Fabrice Vavre, Natacha Kremer Wolbachia load variation in Drosophila is more likely caused by drift than by host genetic factors https://doi.org/10.1101/2020.11.29.402545

Explaining the emergence and maintenance of intratumor heterogeneity is an important question in cancer biology. Tumor cells can generate considerable subclonal diversity, which influences tumor growth rate, treatment resistance, and metastasis, yet we know remarkably little about how cells from different subclones interact. Here, we confronted two murine mammary cancer cell lines to determine both the nature and mechanisms of subclonal cellular interactions in vitro. Surprisingly, we found that, compared to monoculture, growth of the ‘winner’ was enhanced by the presence of the ‘loser’ cell line, whereas growth of the latter was reduced. Mathematical modeling and laboratory assays indicated that these interactions are mediated by the production of paracrine metabolites resulting in the winner subclone effectively ‘farming’ the loser. Our findings add a new level of complexity to the mechanisms underlying subclonal growth dynamics.

Lockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, en masse mitigation has come with substantial socioeconomic costs. In this paper, we demonstrate how individualized policies based on disease status can reduce transmission risk while minimizing impacts on economic outcomes. We design feedback control policies informed by optimal control solutions to modulate interaction rates of individuals based on the epidemic state. We identify personalized interaction rates such that recovered/immune individuals elevate their interactions and susceptible individuals remain at home before returning to pre-lockdown levels. As we show, feedback control policies can yield similar population-wide infection rates to total shutdown but with significantly lower economic costs and with greater robustness to uncertainty compared to optimal control policies. Our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.

of mutated hematopoietic cells Abstract: Classical BCR-ABL -negative myeloproliferative neoplasms (MPN) are clonal disorders of hematopoietic stem cells (HSC) caused mainly by recurrent mutations in genes encoding JAK2 ( JAK2 ), calreticulin (CALR ), or the thrombopoietin receptor ( MPL ). Interferon alpha (IFNα) has demonstrated some efficacy in inducing molecular remission in MPN. In order to determine factors that influence molecular response rate, we evaluated the long-term molecular efficacy of IFNα in MPN patients by monitoring the fate of cells carrying driver mutations in a prospective observational and longitudinal study of 48 patients over more than 5 years. We measured several times per year the clonal architecture of early and late hematopoietic progenitors (84,845 measurements) and the global variant allele frequency in mature cells (409 measurements). Using mathematical modeling and hierarchical Bayesian inference, we further inferred the dynamics of IFNα-targeted mutated HSC. Our data support the hypothesis that IFNα targets JAK2 V617F HSC by inducing their exit from quiescence and differentiation into progenitors. Our observations indicate that treatment efficacy is higher in homozygous than heterozygous JAK2 V617F HSC and increases with high IFNα dosage in heterozygous JAK2 V617F HSC. Besides, we found that the molecular responses of CALR m HSC to IFNα were heterogeneous, varying between type 1 and type 2 CALR m , and high dosage of IFNα correlates with worse outcomes. Together, our work indicates that the long-term molecular efficacy of IFNα implies an HSC exhaustion mechanism and depends on both the driver mutation type and IFNα dosage. Abstract hematopoietic stem cells (HSC) caused mainly by recurrent mutations in genes encoding JAK2 ( JAK2 ), calreticulin (CALR ), or the thrombopoietin receptor ( MPL ). Interferon alpha (IFNα) has demonstrated some efficacy in inducing molecular remission in MPN. In order to determine factors that influence molecular response rate, we evaluated the long-term molecular efficacy of IFNα in MPN patients by monitoring the fate of cells carrying driver mutations in a prospective observational and longitudinal study of 48 patients over more than 5 years. We measured several times per year the clonal architecture of early and late hematopoietic progenitors (84,845 measurements) and the global variant allele frequency in mature cells (409 measurements). Using mathematical modeling and hierarchical Bayesian inference, we further inferred the dynamics of IFNα-targeted mutated HSC. Our data support the hypothesis that IFNα targets JAK2 V617F HSC by inducing their exit from quiescence and differentiation into progenitors. Our observations indicate that treatment efficacy is higher in homozygous than heterozygous JAK2 V617F HSC and increases with high IFNα dosage in heterozygous JAK2 V617F HSC. Besides, we found that the molecular responses of CALR m HSC to IFNα were heterogeneous, varying between type 1 and type 2 CALR m , and high dosage of IFNα correlates with worse outcomes. Together, our work indicates that the long-term molecular efficacy of IFNα implies an HSC exhaustion mechanism and depends on both the driver mutation type and IFNα dosage.

I employ a simple mathematical model of an epidemic process to evaluate how four basic quantities: the reproduction number (R), the numbers of sensitive (S) and infectious individuals(I), and total community size (N) affect strategies to control COVID-19. Numerical simulations show that strict suppression measures at the beginning of an epidemic can create low infectious numbers, which thereafter can be managed by mitigation measures over longer periods to flatten the epidemic curve. The stronger the suppression measure, the faster it achieves the low numbers of infections that are conducive to subsequent management. We discuss the predictions of this analysis and how it fits into longer-term sequences of measures, including using the herd immunity concept to leverage acquired immunity.

Successful therapies to combat microbial diseases and cancers require incorporating ecological and evolutionary principles. Drawing upon the fields of ecology and evolutionary biology, we present a systems-based approach in which host and disease-causing factors are considered as part of a complex network of interactions, analogous to studies of "classical" ecosystems. Centering this approach around empirical examples of disease treatment, we present evidence that successful therapies invariably engage multiple interactions with other components of the host ecosystem. Many of these factors interact nonlinearly to yield synergistic benefits and curative outcomes. We argue that these synergies and nonlinear feedbacks must be leveraged to improve the study of pathogenesis in situ and to develop more effective therapies. An eco-evolutionary systems perspective has surprising and important consequences, and we use it to articulate areas of high research priority for improving treatment strategies.

The COVID-19 pandemic is still in its early stages and given the speed and magnitude of local outbreaks it is urgent to understand how mitigation measures translate into changes in key epidemiological and clinical outcomes. Here, we employ a mathematical model to explore the short-term consequences of lowering the reproduction number number R0 and delaying measures on total infections and fatalities. The positive implications of mitigation generally accrue as these measures are adopted early, with the most striking effects seen when the reproductive number is lowered to a level Rc≈1.0. As the delay in adopting measures exceeds approximately the half-way point to the peak of an outbreak, the effects of lowering R0 markedly decrease. Aiming for reproduction numbers close to 1.0 can substantially reduce fatality probabilities over short time scales, particularly for larger populations. We conclude that research is urgently needed on how mitigation measures impact R0 and how these can be optimized so as to achieve Rc≈1.0 whilst supporting individual freedoms, society and the economy.

The utility of intratumour heterogeneity as a prognostic biomarker is the subject of ongoing clinical investigation. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here, we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate and progression-free survival. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. In cohorts of tumours with diverse evolutionary parameters, we find that clonal diversity is a reliable predictor of both growth rate and progression-free survival. We thus offer explanations-grounded in evolutionary theory-for empirical findings in various cancers, including survival analyses reported in the recent TRACERx Renal study of clear-cell renal cell carcinoma. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.