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Executives who lead cities face considerable challenges. Cities are complex ecosystems comprising myriad interacting systems, including information systems, which are vital for their operation and prosperity. Recognizing the value that information systems can offer, significant investments have been made to acquire these systems and make cities smarter or more intelligent. However, many smart city projects have failed to deliver on their promises due to a lack of clarity regarding their overall goals. Smarter or more intelligent towards what end? This article provides a systems-inspired approach to urban planning based on the premise that a city is a self-organising, capital-creating ecosystem composed of a city's administration and the many organizations within its domain. As the city's keystone entity, the administration's role is to create capital that enhances the capital productivity of other ecosystem members and nourishes innovators to boost capital creativity. A capital improvement budgeting framework can specify the allocation of all forms of capital to promote symbiotic connections between the various entities within a city. A city stack is designed to guide a city's administration in cooperating with its self-organising ecosystem. A research agenda to foster capital creation by city leaders is also outlined.

Evolution by natural selection is believed to be the only possible source of spontaneous adaptive organisation in the natural world. This places strict limits on the kinds of systems that can exhibit adaptation spontaneously, i.e., without design. Physical systems can show some properties relevant to adaptation without natural selection or design. (1) The relaxation, or local energy minimisation, of a physical system constitutes a natural form of optimisation insomuch as it finds locally optimal solutions to the frustrated forces acting on it or between its components. (2) When internal structure ‘gives way’ or accommodates a pattern of forcing on a system, this constitutes learning insomuch, as it can store, recall, and generalise past configurations. Both these effects are quite natural and general, but in themselves insufficient to constitute non-trivial adaptation. However, here we show that the recurrent interaction of physical optimisation and physical learning together results in significant spontaneous adaptive organisation. We call this adaptation by natural induction. The effect occurs in dynamical systems described by a network of viscoelastic connections subject to occasional disturbances. When the internal structure of such a system accommodates slowly across many disturbances and relaxations, it spontaneously learns to preferentially visit solutions of increasingly greater quality (exceptionally low energy). We show that adaptation by natural induction thus produces network organisations that improve problem-solving competency with experience (without supervised training or system-level reward). We note that the conditions for adaptation by natural induction, and its adaptive competency, are different from those of natural selection. We therefore suggest that natural selection is not the only possible source of spontaneous adaptive organisation in the natural world.

RNA folding free energy nearest neighbor parameters were determined for sequences with the nucleotide m6A. The RNAstructure software package can accommodate modified nucleotides, enabling secondary structure prediction of sequences with m6A.

Abstract Background Biological evolution exhibits an extraordinary capability to adapt organisms to their environments. The explanation for this often takes for granted that random genetic variation produces at least some beneficial phenotypic variation in which natural selection can act. Such genetic evolvability could itself be a product of evolution, but it is widely acknowledged that the immediate selective gains of evolvability are small on short timescales. So how do biological systems come to exhibit such extraordinary capacity to evolve? One suggestion is that adaptive phenotypic plasticity makes genetic evolution find adaptations faster. However, the need to explain the origin of adaptive plasticity puts genetic evolution back in the driving seat, and genetic evolvability remains unexplained. Results To better understand the interaction between plasticity and genetic evolvability, we simulate the evolution of phenotypes produced by gene-regulation network-based models of development. First, we show that the phenotypic variation resulting from genetic and environmental perturbation are highly concordant. This is because phenotypic variation, regardless of its cause, occurs within the relatively specific space of possibilities allowed by development. Second, we show that selection for genetic evolvability results in the evolution of adaptive plasticity and vice versa. This linkage is essentially symmetric but, unlike genetic evolvability, the selective gains of plasticity are often substantial on short, including within-lifetime, timescales. Accordingly, we show that selection for phenotypic plasticity can be effective in promoting the evolution of high genetic evolvability. Conclusions Without overlooking the fact that adaptive plasticity is itself a product of genetic evolution, we show how past selection for plasticity can exercise a disproportionate effect on genetic evolvability and, in turn, influence the course of adaptive evolution.

Abstract Algorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.

The truly surprising thing about evolution is not how it makes individuals better adapted to their environment, but how it makes individuals. All individuals are made of parts that used to be individuals themselves, e.g., multicellular organisms from unicellular organisms. In such evolutionary transitions in individuality, the organised structure of relationships between component parts causes them to work together, creating a new organismic entity and a new evolutionary unit on which selection can act. However, the principles of these transitions remain poorly understood. In particular, the process of transition must be explained by “bottom-up” selection, i.e., on the existing lower-level evolutionary units, without presupposing the higher-level evolutionary unit we are trying to explain. In this hypothesis and theory manuscript we address the conditions for evolutionary transitions in individuality by exploiting adaptive principles already known in learning systems. Connectionist learning models, well-studied in neural networks, demonstrate how networks of organised functional relationships between components, sufficient to exhibit information integration and collective action, can be produced via fully-distributed and unsupervised learning principles, i.e., without centralised control or an external teacher. Evolutionary connectionism translates these distributed learning principles into the domain of natural selection, and suggests how relationships among evolutionary units could become adaptively organised by selection from below without presupposing genetic relatedness or selection on collectives. In this manuscript, we address how connectionist models with a particular interaction structure might explain transitions in individuality. We explore the relationship between the interaction structures necessary for (a) evolutionary individuality (where the evolution of the whole is a non-decomposable function of the evolution of the parts), (b) organismic individuality (where the development and behaviour of the whole is a non-decomposable function of the behaviour of component parts) and (c) non-linearly separable functions, familiar in connectionist models (where the output of the network is a non-decomposable function of the inputs). Specifically, we hypothesise that the conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions of this type (or deep model induction) familiar in connectionist models of cognition and learning.

For decades, the potential role of probiotics in the prevention and treatment of recurrent urinary tract infections has been extensively studied. However, achieving an effective problem-solving strategy has thus far proven elusive. Perhaps adopting a military paradigm might expedite our assault on chronic, recurring bacteriuria in women. What is needed is a targeted strategy with specific attention to (1) the enemy: the case-specific uropathogen; (2) the battlefield: the extraordinarily complex interplay of factors within the bladder, unique to a given patient, which interface with profoundly important influences from the gut biome, as well as the vaginal biota; (3) the weapon: an antimicrobial probiotic with demonstrated activity against that specific uropathogen; (4) a new strategy: taking these complexities into account, we posit a key role for the instillation of case-specific lactobacilli directly into the bladder of the designated patient. This newly proposed, targeted intervention might be termed “Probiotic Intravesical Organic Therapy—PIVOT”; and (5) the long campaign: reaching clinically proven success may entail a long campaign. However, already, on many fronts, the elements necessary for victory recently seem to be falling into place.

Kin selection theory and multilevel selection theory are distinct approaches to explaining the evolution of social traits. The latter claims that it is useful to regard selection as a process that can occur on multiple levels of organisation such as the level of individuals and the level of groups. This is reflected in a decomposition of fitness into an individual component and a group component. This multilevel view is central to understanding and characterising evolutionary transitions in individuality, e.g., from unicellular life to multicellular organisms, but currently suffers from the lack of a consistent, quantifiable measure. Specifically, the two major statistical tools to determine the coefficients of such a decomposition, the multilevel Price equation and contextual analysis, are inconsistent and may disagree on whether group selection is present. Here we show that the reason for the discrepancies is that underlying the multilevel Price equation and contextual analysis are two non-equivalent causal models for the generation of individual fitness effects (thus leaving different “remainders” explained by group effects). While the multilevel Price equation assumes that the individual effect of a trait determines an individual's relative success within a group, contextual analysis posits that the individual effect is context-independent. Since these different assumptions reflect claims about the causal structure of the system, the correct approach cannot be determined on general theoretical or statistical grounds but must be identified by experimental intervention. We outline interventions that reveal the underlying causal structure and thus facilitate choosing the appropriate approach. We note that kin selection theory with its focus on the individual is immune to such inconsistency because it does not address causal structure with respect to levels of organisation. In contrast, our analysis of the two approaches to measuring group selection demonstrates that multilevel selection theory adds meaningful (falsifiable) causal structure to explain the sources of individual fitness and thereby constitutes a proper refinement of kin selection theory. Taking such refined causal structure into account seems indispensable for studying evolutionary transitions in individuality because these transitions are characterised by changes in the selection pressures that act on the respective levels.

Understanding the spatial context of animal movements is fundamental for the establishment and management of protected areas. We tracked, by telemetry devices, 31 captive-reared and 5 wild-rehabilitated Harpia harpyja and estimated the dispersal and space use after release in Mesoamerica. We evaluated the effectiveness of protected areas in the protection of home ranges and examined how individual traits, release methods and landscape features influenced the dispersal and home range using mixed-effects models. The mean post-release dispersal was 29.4 km (95% CI: 22.5–38.5), and the annual home ranges averaged 1039.5 km2 (95% CI: 627–1941). The home ranges were influenced by the release method, patch richness, patch and edge density and contagion. The currently protected areas in Mesoamerica may not be effective conservation units for this species. The Harpy Eagle average home range greatly exceeded the average size of 1115 terrestrial protected areas (52.7 ± 6.1 km2) in Mesoamerica. Due to their wide use of space, including transboundary space, Harpy Eagle conservation efforts may fail if they are not carefully coordinated between the countries involved. Future restoration efforts of umbrella forest-dwelling raptors should select release sites with highly aggregated and poorly interspersed forests. The release sites should have a buffer of approximately 30 km and should be located completely within protected areas.

Cell differentiation in multicellular organisms requires cells to respond to complex combinations of extracellular cues, such as morphogen concentrations. Some models of phenotypic plasticity conceptualise the response as a relatively simple function of a single environmental cues (e.g. a linear function of one cue), which facilitates rigorous analysis. Conversely, more mechanistic models such those implementing GRNs allows for a more general class of response functions but makes analysis more difficult. Therefore, a general theory describing how cells integrate multi-dimensional signals is lacking. In this work, we propose a theoretical framework for understanding the relationships between environmental cues (inputs) and phenotypic responses (outputs) underlying cell plasticity. We describe the relationship between environment and cell phenotype using logical functions, making the evolution of cell plasticity equivalent to a simple categorisation learning task. This abstraction allows us to apply principles derived from learning theory to understand the evolution of multi-dimensional plasticity. Our results show that natural selection is capable of discovering adaptive forms of cell plasticity associated with complex logical functions. However, developmental dynamics cause simpler functions to evolve more readily than complex ones. By using conceptual tools derived from learning theory we show that this developmental bias can be interpreted as a learning bias in the acquisition of plasticity functions. Because of that bias, the evolution of plasticity enables cells, under some circumstances, to display appropriate plastic responses to environmental conditions that they have not experienced in their evolutionary past. This is possible when the selective environment mirrors the bias of the developmental dynamics favouring the acquisition of simple plasticity functions-an example of the necessary conditions for generalisation in learning systems. These results illustrate the functional parallelisms between learning in neural networks and the action of natural selection on environmentally sensitive gene regulatory networks. This offers a theoretical framework for the evolution of plastic responses that integrate information from multiple cues, a phenomenon that underpins the evolution of multicellularity and developmental robustness.

Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these can require sophisticated analysis techniques. This research was focused on carrying out a sentiment analysis from social surveys. Data analysis techniques using RStudio and Python were applied to several open-source datasets, which included the 2018 Social Indicators Survey dataset published by the City of Melbourne (CoM) and the Casey Next short survey 2016 dataset published by the City of Casey (CoC). The qualitative nature of the CoC dataset responses could produce rich insights using sentiment analysis, unlike the quantitative CoM dataset. RStudio analysis created word cloud visualizations and bar charts for sentiment values. These were then used to inform social media analysis via the Twitter application programming interface. The R codes were all integrated within a Shiny application to create a set of user-friendly interactive web apps that generate sentiment analysis both from the historic survey data and more immediately from the Twitter feeds. The web apps were embedded within a website that provides a customisable solution to estimate sentiment for key issues. Global sentiment was also compared between the social media approach and the 2016 survey dataset analysis and showed some correlation, although there are caveats on the use of social media for sentiment analysis. Further refinement of the methodology is required to improve the social media app and to calibrate it against analysis of recent survey data.

Abstract Greenhouse agriculture is a highly efficient method of food production that can greatly benefit from supplemental electric lighting. The needed electricity associated with greenhouse lighting amounts to about 30% of its operating costs. As the light level of LED lighting can be easily controlled, it offers the potential to reduce energy costs by precisely matching the amount of supplemental light provided to current weather conditions and a crop’s light needs. Three simulations of LED lighting for growing lettuce in the south-east of the US using historical solar radiation data for the area were conducted. Lighting costs can be potentially reduced by approximately 60%.

Experiments have been performed to demonstrate that ultrasound in the 100–400 kHz frequency range can be used to propagate signals through various types of industrial insulation. This is despite the fact that they are highly attenuating to ultrasonic signals due to scattering and viscoelastic effects. The experiments used a combination of piezocomposite transducers and pulse compression processing. This combination allowed signal-to-noise levels to be enhanced so that signals reflected from the surface of an insulated and cladded steel pipe could be obtained.

One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour developmental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regularities, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting 'quick fixes' (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the subsequent failure to generalise. We support the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that existing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well-developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.

BACKGROUND: Knowledge of factors and mechanisms contributing to the inherent radioresistance of pancreatic cancer may improve cancer treatment. Irradiation inhibits glycogen synthase kinase 3β (GSK3β) by phosphorylation at serine 9. In turn, release of cytosolic membrane β-catenin with subsequent nuclear translocation promotes survival. Both GSK3β and β-catenin have been implicated in cancer cell proliferation and resistance to death. METHODS: We investigated pancreatic cancer cell survival after radiation in vitro and in vivo, with a particular focus on the role of the function of the GSK3β/β-catenin axis. RESULTS: Lithium chloride, RNAi-medicated silencing of GSK3β, or the expression of a kinase dead mutant GSK3β resulted in radioresistance of Panc1 and BxPC3 pancreatic cancer cells. Conversely, ectopic expression of a constitutively active form of GSK3β resulted in radiosensitization of Panc1 cells. GSK3β silencing increased radiation-induced β-catenin target gene expression asmeasured by studies of AXIN2 and LEF1 transcript levels. Western blot analysis of total and phosphorylated levels of GSK3β and β-catenin showed that GSK3β inhibition resulted in stabilization of β-catenin. Xenografts of both BxPC3 and Panc1 with targeted silencing of GSK3β exhibited radioresistance in vivo. Silencing of β-catenin resulted in radiosensitization, whereas a nondegradable β-catenin construct induced radioresistance. CONCLUSIONS: These data support the hypothesis that GSK3β modulates the cellular response to radiation in a β-catenin-dependent mechanism. Further understanding of this pathway may enhance the development of clinical trials combining drugs inhibiting β-catenin activation with radiation and chemotherapy in locally advanced pancreatic cancer.

BACKGROUND: Treatment with inhaled glucocorticosteroids attenuates allergen-induced airway inflammation but is less effective in people with asthma who have noneosinophilic airway inflammation.

The Internet of Things (IoT) is an extension of the Internet in which large numbers of “things”, including sensors, actuators and processors, in addition to human users, are networked and able to provide high resolution data on their environment and exercise a degree of control over it. It is still at an early stage of development, and many problems/research challenges must be solved before it is widely adopted. Many of these are technical, including interoperability and scalability, as billions of heterogeneous devices will be connected, but deciding on how to invest in the IoT is a challenge for business, and there are also major social, legal and ethical challenges, including security and privacy of data collection, which must be resolved. As the future IoT will be a multi-national, multi-industry, multi-technology infrastructure, the paper reviews the global standardization efforts that are underway to facilitate its worldwide creation and adoption. The main purpose of the paper is to give a broad survey, based on published literature, of the methods of Operations Research (OR), both the mathematical tools and techniques of “hard” OR, and the various approaches of Systems Thinking, including “soft” OR, which may assist in dealing with these problems. A subset of these is described in greater depth to better convey what might be involved in applying OR and Systems Thinking to the IoT. It is suggested that OR has a role to play in balancing the technical and non-technical research challenges which confront the IoT.

Human consumers of wildlife killed with lead ammunition may be exposed to health risks associated with lead ingestion. This hypothesis is based on published studies showing elevated blood lead concentrations in subsistence hunter populations, retention of ammunition residues in the tissues of hunter-killed animals, and systemic, cognitive, and behavioral disorders associated with human lead body burdens once considered safe. Our objective was to determine the incidence and bioavailability of lead bullet fragments in hunter-killed venison, a widely-eaten food among hunters and their families. We radiographed 30 eviscerated carcasses of White-tailed Deer (Odocoileus virginianus) shot by hunters with standard lead-core, copper-jacketed bullets under normal hunting conditions. All carcasses showed metal fragments (geometric mean = 136 fragments, range = 15-409) and widespread fragment dispersion. We took each carcass to a separate meat processor and fluoroscopically scanned the resulting meat packages; fluoroscopy revealed metal fragments in the ground meat packages of 24 (80%) of the 30 deer; 32% of 234 ground meat packages contained at least one fragment. Fragments were identified as lead by ICP in 93% of 27 samples. Isotope ratios of lead in meat matched the ratios of bullets, and differed from background lead in bone. We fed fragment-containing venison to four pigs to test bioavailability; four controls received venison without fragments from the same deer. Mean blood lead concentrations in pigs peaked at 2.29 microg/dL (maximum 3.8 microg/dL) 2 days following ingestion of fragment-containing venison, significantly higher than the 0.63 microg/dL averaged by controls. We conclude that people risk exposure to bioavailable lead from bullet fragments when they eat venison from deer killed with standard lead-based rifle bullets and processed under normal procedures. At risk in the U.S. are some ten million hunters, their families, and low-income beneficiaries of venison donations.

In this paper an empirical investigation is reported of the attitudes of purchasing managers to the degree of wrongness of a range of ethically problematical issues and what the corresponding punishment should be for these actions. The study found a significant difference between what purchasing managers believe is wrong and how willing they are to punish transgressors. It is suggested that the largest difference between a wrongful action and its punishment occurs when the action is clearly wrong, but the financial impact upon the employer is minor. The paper concludes by suggesting areas for future research that could explore why there is a difference between a crime and its associated punishment.