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In this article, we identify challenges in the complex interaction between artificial intelligence (AI) systems and society. We argue that AI systems need to be studied in their socio-political context to be able to better appreciate a diverse set of potential outcomes that emerge from long-term feedback between technological development, inequalities and collective decision-making processes. This means that assessing the risks from the deployment of any specific technology presents unique challenges. We propose that risk assessments concerning AI systems should incorporate a complex systems perspective, with adequate models that can represent short- and long-term effects and feedback, along with an emphasis on increasing public engagement and participation in the process. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented on the study which examine the levels of triiodothyronine in patients with Alzheimer's disease.

Though algorithms promise many benefits including efficiency, objectivity and accuracy, they may also introduce or amplify biases. Here we study two well-known algorithms, namely PageRank and Who-to-Follow (WTF), and show to what extent their ranks produce inequality and inequity when applied to directed social networks. To this end, we propose a directed network model with preferential attachment and homophily (DPAH) and demonstrate the influence of network structure on the rank distributions of these algorithms. Our main findings suggest that (i) inequality is positively correlated with inequity, (ii) inequality is driven by the interplay between preferential attachment, homophily, node activity and edge density, and (iii) inequity is driven by the interplay between homophily and minority size. In particular, these two algorithms reduce, replicate and amplify the representation of minorities in top ranks when majorities are homophilic, neutral and heterophilic, respectively. Moreover, when this representation is reduced, minorities may improve their visibility in the rank by connecting strategically in the network. For instance, by increasing their out-degree or homophily when majorities are also homophilic. These findings shed light on the social and algorithmic mechanisms that hinder equality and equity in network-based ranking and recommendation algorithms. [ABSTRACT FROM AUTHOR]

Objective: Non-invasive brain stimulation (NIBS) methodologies, such as transcranial electric (tES) are increasingly employed for therapeutic, diagnostic, or research purposes. The concurrent presence of active/passive implants can pose safety risks, affect the NIBS delivery, or generate confounding signals. A systematic investigation is required to understand the interaction mechanisms, quantify exposure, assess risks, and establish guidance for NIBS applications., Approach: We used measurements, simplified generic, and detailed anatomical modeling to: (i) systematically analyze exposure conditions with passive and active implants, considering local field enhancement, exposure dosimetry, tissue heating and neuromodulation, capacitive lead current injection, low-impedance pathways between electrode contacts, and insulation damage; (ii) identify risk metrics and efficient prediction strategies; (iii) quantify these metrics in relevant exposure cases and (iv) identify worst case conditions. Various aspects including implant design, positioning, scar tissue formation, anisotropy, and frequency were investigated., Results: At typical tES frequencies, local enhancement of dosimetric exposure quantities can reach up to one order of magnitude for deep brain stimulation (DBS) and stereoelectroencephalography implants (more for elongated passive implants), potentially resulting in unwanted neuromodulation that can confound results but is still 2-3 orders of magnitude lower than active DBS. Under worst-case conditions, capacitive current injection in the active implants' lead can produce local exposures of similar magnitude as the passive field enhancement, while capacitive pathways between contacts are negligible. Above 10 kHz, applied current magnitudes increase, necessitating consideration of tissue heating. Furthermore, capacitive effects become more prominent, leading to current injection that can reach DBS-like levels. Adverse effects from abandoned/damaged leads in direct electrode vicinity cannot be excluded., Significance: Safety related concerns of tES application in the presence of implants are systematically identified and explored, resulting in specific and quantitative guidance and establishing basis for safety standards. Furthermore,several methods for reducing risks are suggested while acknowledging the limitations(see Sec. 4.5)., (Creative Commons Attribution license.)

Migration's impact spans various social dimensions, including demography, sustainability, politics, economy, and gender disparities. Yet, the decision-making process behind migrants choosing their destination remains elusive. Existing models primarily rely on population size and travel distance to explain the spatial patterns of migration flows, overlooking significant population heterogeneities. Paradoxically, migrants often travel long distances and to smaller destinations if their diaspora is present in those locations. To address this gap, we propose the diaspora model of migration, incorporating intensity (the number of people moving to a country), and assortativity (the destination within the country). Our model considers only the existing diaspora sizes in the destination country, influencing the probability of migrants selecting a specific residence. Despite its simplicity, our model accurately reproduces the observed stable flow and distribution of migration in Austria (postal code level) and US metropolitan areas, yielding precise estimates of migrant inflow at various geographic scales. Given the increase in international migrations, this study enlightens our understanding of migration flow heterogeneities, helping design more inclusive, integrated cities., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Implicit biases - differential attitudes towards members of distinct groups - are pervasive in human societies and create inequities across many aspects of life. Recent research has revealed that implicit biases are generally driven by social contexts, but not whether they are systematically influenced by the ways that humans self-organize in cities. We leverage complex system modeling in the framework of urban scaling theory to predict differences in these biases between cities. Our model links spatial scales from city-wide infrastructure to individual psychology to predict that cities that are more populous, more diverse, and less segregated are less biased. We find empirical support for these predictions in U.S. cities with Implicit Association Test data spanning a decade from 2.7 million individuals and U.S. Census demographic data. Additionally, we find that changes in cities' social environments precede changes in implicit biases at short time-scales, but this relationship is bi-directional at longer time-scales. We conclude that the social organization of cities may influence the strength of these biases., (© 2024. The Author(s).)

Nominal assortativity (or discrete assortativity) is widely used to characterize group mixing patterns and homophily in networks, enabling researchers to analyze how groups interact with one another. Here we demonstrate that the measure presents severe shortcomings when applied to networks with unequal group sizes and asymmetric mixing. We characterize these shortcomings analytically and use synthetic and empirical networks to show that nominal assortativity fails to account for group imbalance and asymmetric group interactions, thereby producing an inaccurate characterization of mixing patterns. We propose the adjusted nominal assortativity and show that this adjustment recovers the expected assortativity in networks with various level of mixing. Furthermore, we propose an analytical method to assess asymmetric mixing by estimating the tendency of inter- and intra-group connectivities. Finally, we discuss how this approach enables uncovering hidden mixing patterns in real-world networks., (© 2023. The Author(s).)

Background: Recent investigations have highlighted autoimmune origins and abnormal immune responses; particularly those related to T cell-mediated immunity and elevated T lymphocyte cells in the oral mucosa. Therefore, we investigated the relationship between recurrent aphthous stomatitis (RAS) and autoimmune thyroid diseases (ATDs) in an Iranian population., Methods: A cross-sectional study was performed on 102 patients diagnosed with ATD (cases) and 102 healthy patients (controls) who had been referred for the routine dental treatment. All participants were asked for the history of RAS and their age, gender, other systemic diseases, medications, and frequency of RAS in a year. Matching was performed based on the propensity scores for age and sex. In addition, the number of lesions in each recurrence in both groups was assessed and compared. The type of thyroid disease has been assessed for case participants and has been confirmed by the endocrinologist. The chi-square test, t-test, and Mann-Whitney U test were used to analyze the data using SPSS 18., Results: Patients with ATD had higher RAS than healthy controls (P = 0.040). ATD patients had 1.93 times more risk for RAS, and the frequency of RAS in a year was 3.15times higher in these patients (P = 0.011). Moreover, the frequency of RAS was higher in patients with hypothyroidism than in those with hyperthyroidism. However, there were no significant differences in the size and the number of lesions between the groups., Conclusion: The risk and frequency of RAS were significantly higher in patients with ATDs. This would provide valuable insights into the underlying mechanisms and potential treatment strategies for both conditions., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Monitoring intracranial pressure (ICP) and craniospinal compliance (CC) is frequently required in the treatment of patients suffering from craniospinal diseases. However, current approaches are invasive and cannot provide continuous monitoring of CC. Dynamic exchange of blood and cerebrospinal fluid (CSF) between cranial and spinal compartments due to cardiac action transiently modulates the geometry and dielectric properties of the brain. The resulting impedance changes can be measured and might be usable as a non-invasive CC surrogate. A numerically robust and computationally efficient approach based on the reciprocity theorem was developed to compute dynamic impedance changes resulting from small geometry and material property changes. The approach was successfully verified against semi-analytical benchmarks, before being combined with experimental brain pulsation data to study the information content of the impedance variation. The results indicate that the measurable signal is dominated by the pulsatile displacement of the cortical brain surface, with minor contributions from the ventricular surfaces and from changes in brain perfusion. Different electrode setups result in complementary information. The information content from the investigated three electrode pairs was employed to successfully infer subject-specific brain pulsation and motion features. This suggests that non-invasive CC surrogates based on impedance monitoring could be established., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Andreas Spiegelberg is applicant and inventor of the patent application DE102018100697A1 and several dependent international applications. Vartan Kurtcuoglu is inventor and the University of Zurich applicant of the same patent applications., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Interaction computing is inspired by the observation that cell metabolic/regulatory systems construct order dynamically, through constrained interactions between their components and based on a wide range of possible inputs and environmental conditions. The goals of this work are to (i) identify and understand mathematically the natural subsystems and hierarchical relations in natural systems enabling this and (ii) use the resulting insights to define a new model of computation based on interactions that is useful for both biology and computation. The dynamical characteristics of the cellular pathways studied in systems biology relate, mathematically, to the computational characteristics of automata derived from them, and their internal symmetry structures to computational power. Finite discrete automata models of biological systems such as the lac operon, the Krebs cycle and p53-mdm2 genetic regulation constructed from systems biology models have canonically associated algebraic structures (their transformation semigroups). These contain permutation groups (local substructures exhibiting symmetry) that correspond to 'pools of reversibility'. These natural subsystems are related to one another in a hierarchical manner by the notion of 'weak control'. We present natural subsystems arising from several biological examples and their weak control hierarchies in detail. Finite simple non-Abelian groups are found in biological examples and can be harnessed to realize finitary universal computation. This allows ensembles of cells to achieve any desired finitary computational transformation, depending on external inputs, via suitably constrained interactions. Based on this, interaction machines that grow and change their structure recursively are introduced and applied, providing a natural model of computation driven by interactions.