bluuebunny/crossref_metadata_2025_split huggingface dataset:
Embodied Intelligence in soft robotics: joys and sorrows
Authors: Laschi Cecilia 
Published: October 2022

Abstract
Embodied intelligence and soft robotics are strictly interconnected, as soft bodyware is key to obtain emergent behaviours from external interactions. The overall advantages of embodied intelligence in robotics are balanced by additional challenges in robot design and control. A main contribution of embodied intelligence to robotics consists of simplifying principles for robot control, in terms of control parameters. At the same time, robot design becomes more complex, involving the complex dynamic interaction of the robot with the environment and the task. These two form the ecological niche where embodied intelligence is effective, but they also limit the robot task space. To what extent can we enlarge the ecological niche without missing the benefits of embodied intelligence? Emergent behaviours are what we expect to simplify control, but what level of controllability can we still achieve? And how far are we from a mathematical description of embodied intelligence, to the benefit of soft robot design and control? Few trade-offs between contrasting needs in the use of embodied intelligence in soft robotics are discussed here, in view of directions of future progress.
Taking Shape: A Perspective on the Future of Embodied Cognition and a new Generation of Evolutionary Robotics
Authors: Howard David , Collins Jack , Robinson Nicole 
Published: October 2022

Abstract
The philosophy of Embodied Cognition (EC) presents an intuitive lens with which to consider a variety of problems that attempt to optimise or refine some physically-grounded combination of form and function. Conceptualised as a Venn diagram, with circles of 'body', 'brain', and 'environment', the prevailing notion is that moving to the centre of the diagram is the ultimate goal - intertwining and leveraging all three components to generate adaptive solutions. Philosophically, at least, this makes sense: nature abounds with examples of life that extol the virtues of tightly-coupled embodiment and hint at the possibilities attainable when designing entities through EC principles. The algorithmic basis for following this approach is similarly intuitive: extra degrees of freedom to the design process combined with explicit consideration of the environment allow for a wider range of interesting, useful behaviours. However, moving from philosophy to concrete algorithmic implementation presents a number of pitfalls and barriers that have prevented EC from being more ubiquitously applied as a mainstream problem-solving methodology. In this comment, we speculate on one possible avenue for the future of EC wherein fuller implementations of EC are enabled through adoption of algorithmic advances from the neighbouring field of Machine Learning. Further, we suggest to re-frame evolutionary robotics as a model learning problem, wherein the end goal is to generate an accurate design landscape through the application of high-throughput techniques and tightly coupled digital-experimental systems. Combined, these techniques offer the possibility to reinvent the state of the art and hint at a bright future for evolutionary robotics.
Meta-brain Models: biologically-inspired cognitive agents
Authors: Alicea B , Parent J 
Published: October 2022

Abstract
Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.
Active inference, preference learning and adaptive behaviour
Authors: Sajid Noor , Tigas Panagiotis , Friston Karl 
Published: October 2022

AbstractThe ability to adapt to a changing environment underwrites sentient behaviour e.g., wearing a raincoat when walking in the rain but removing it when indoors. In such instances, agents act to satisfy some preferred mode of behaviour that leads to predictable states necessary for survival, i.e., states that are characteristic of that agent. In this chapter, we describe how active inference agents, equipped with preference learning, can exhibit these distinct behavioural modes – influenced by environment dynamics – to aptly trade-off between preference satisfaction and exploration. We validate this in a modified OpenAI Gym FrozenLake environment (without any extrinsic signal) with and without volatility under a fixed model of the environment. In a static (i.e., without volatility) environment, preference-learning agents accumulate confident (Bayesian) beliefs about their behaviour and act to satisfy them. In contrast, volatile dynamics led to preference uncertainty and exploratory behaviour. This demonstrates that active inference agents, equipped with preference learning, have the appropriate machinery to (i) engage in adaptive behaviour under appropriate levels of volatility, and (ii) learn context-dependent subjective preferences.
The deep time perspective: How insights from millennia of evolving and extinct animals can inform robot design decision making
Authors: Carter Aja , Roberts Sonia 
Published: October 2022

AbstractAnimals on the visible scale have been experimenting with body form and function in enumerable enviroments for the last 540 million years. Almost all of the variation in the history of life is now gone, leaving only a tiny fraction of what is possible alive in modern animals. Recently biological and engineering techniques have made it possible to robustly answer questions only the fossil record can provide, such as the history and original functions of certain behaviors. Robotics has already begun to champion inspiration from biology, but only from the small variation seen in extant taxa. In this chapter we introduce paleontological study of a now famous source of bioinspiration: The modern African Cheetah. We discuss additional forms of high-speed pursuit predators from the lineage that demonstrate an evolutionary experiment in manuverability on uneven terrain. We also discuss how the evolution of sensing and perception does not always follow or work in concert with the evolution of the musculoskeletal system. We end by summarizing the process of our analysis so that our methods can be repeated for other animals that inspire engineers. We hope this brief primer introduces paleobiology to roboticists and demonstrates the need for biologically inspired robotics to engage with paleobiological processes, to the benefit of both robotics in developing new technology and paleobiology in answering long-standing questions about relationships between extinct forms and their functions.
What Could Models of Superorganismal Cognition Offer to Embodied AI?
Authors: Hunt Edmund R. 
Published: October 2022

Abstract
Superorganisms such as ant or honeybee colonies exhibit extraordinary collective intelligence, such as an ability to identify and choose the best available nest site in an uncertain world. This collective cognition is inextricably reliant on the embodiment of individual agents, specifically their movement through space. We have recently developed models of superorganismal cognition based on a compelling analogy with techniques in Bayesian statistics, which are likewise aimed at grappling with the uncertainty and incompleteness of real data sources. These models foreground some potential lessons for the design of embodied artificial intelligences, such as robot swarms. For example, the spatial distribution of independently judging agents can convey valuable information, relaxing expectations that regular inter-agent (‘inter-neuronal’) communication is necessary for cognition, which points to the potential of minimal field swarm robotics. Meanwhile, the importance of individual heterogeneity to effective and resilient collective cognition in biology suggests great potential in this area for engineering.
Embodied Intelligence in Physical, Social and Technological Environments
Authors: Straub Ilona 
Published: October 2022

Abstract
The aim of this contribution is to highlight different aspects of embodied intelligence that add to a broader interdisciplinary perspective on this phenomenon. This article intends to bring together three forms of embodied intelligence from the fields of technology and social research, starting from robotics research that deals with the technical replication of organic bodies, to insights from the sociology of the body outlining the relevance of corporeality in social encounters, and finally to the forms of embodied intelligence in the connection of technical forms with organic bodies and their effects on the perception of self and others within the hybrid research field of "Bodies of Technologies". This tripartite division of embodied intelligence deals with the biophysiological properties of an organism in self-environment relationships (the subject of robotics research), the interaction of a being or system with other beings in self-environment relationships (the subject of the sociology of bodies), and finally, as a third, new level of embodied intelligence, the technically mediated experience of (virtual) self-environment relationships through the somatechnical fusion of bio- and socio-physical properties in human-machine hybrids is presented. Accordingly, the article will name the specificity of each form of embodied intelligence and trace the conditions and peculiarities in the gradual increase of complexity from simple to interactive and finally to virtual bodily intelligence.
Non-invasive assessment of intestinal permeability in healthy volunteers using transcutaneous fluorescence spectroscopy
Authors: Gan Jonathan , Monfort Sánchez Elena , Avery James , Barbouti Omar , Hoare Jonathan , Ashrafian Hutan , Darzi Ara , Thompson Alex J 
Published: October 2022

Abstract
The permeability of the intestinal barrier is altered in a multitude of gastrointestinal conditions such as Crohn’s and coeliac disease. However, the clinical utility of gut permeability is currently limited due to a lack of reliable diagnostic tests. To address this issue, we report a novel technique for rapid, non-invasive measurement of gut permeability based on transcutaneous (‘through-the-skin’) fluorescence spectroscopy. In this approach, participants drink an oral dose of a fluorescent dye (fluorescein) and a fibre-optic fluorescence spectrometer is attached to the finger to detect permeation of the dye from the gut into the blood stream in a non-invasive manner. To validate this technique, clinical trial measurements were performed in 11 healthy participants. First, after 6 h of fasting, participants ingested 500 mg of fluorescein dissolved in 100 ml of water and fluorescence measurements were recorded at the fingertip over the following 3 h. All participants were invited back for a repeat study, this time ingesting the same solution but with 60 g of sugar added (known to transiently increase intestinal permeability). Results from the two study datasets (without and with sugar respectively) were analysed and compared using a number of analysis procedures. This included both manual and automated calculation of a series of parameters designed for assessment of gut permeability. Calculated values were compared using Student’s T-tests, which demonstrated significant differences between the two datasets. Thus, transcutaneous fluorescence spectroscopy shows promise in non-invasively discriminating between two differing states of gut permeability, demonstrating potential for future clinical use.
Fabricating a slippery porous nickel coating based on the hydrogen bubble template method for corrosion protection with self-healing property
Authors: Ma Fuliang , Shen Luli , Zeng Zhixiang 
Published: October 2022

Abstract
In recent years, the development of the slippery liquid-infused porous surface (SLIPS) for metal corrosion protection has attracted extensive attention. The current construction method of SLIPS micro-nano structure has certain limitations. In this paper, a porous structure was firstly constructed by electroplating nickel based on the hydrogen bubble template method, and then the porous structure was modified by 1H, 1H, 2H, 2H-perfluorodecyltriethoxysilane (PFDS) to obtain the superhydrophobic coating, and finally, The SLIPS coating was obtained by infusing perfluoropolyether (PFPE) on the basis of superhydrophobic coating. After immersed in 3.5 wt% NaCl solution for 4 weeks, the WCA of SLIPS constructed in this paper only decreased from 121° to 114°. Importantly, the corrosion resistance of SLIPS was enhanced by one and three orders of magnitude compared with SHC and Cu substrate. In addition, it was worth mentioning that the SLIPS also exhibited a superior self-healing property even after several scratching healing cycles. The simple method of building porous structures in this paper will promote the further development of the SLIPS coating and its application in engineering.
Disordered packings of binary mixtures of dimer particles
Authors: Kurban Esma , Baule Adrian 
Published: October 2022

Abstract
Disordered packings of non-spherical particles and their mixtures are abundant in nature, but have so far attracted only few systematic studies. Previous investigations of binary mixtures of specific convex shapes have established two generic properties: (i) the existence of a unique density maximum when shape or mixture composition of the two species are varied; (ii) the validity of an ideal mixing law indicating that the packing density is independent of the segregation state. These findings were so far only observed for mixtures of convex particles such as spherocylinders, ellipsoids, and spheres. Here, we investigate the packing properties of binary mixtures of frictionless dimer particles simulated by a gravitational pouring protocol in LAMMPS. Our results demonstrate the validity of (i, ii) also for such packings of non-convex particles. Moreover, we investigate the contact statistics of these packings to elucidate the microstructural features that underlie (i, ii). Our results show that the contact number per species also satisfies a simple mixing law and that similar microscopic rearrangements of contacts as in monodisperse dimer packings accompany the formation of the density peak in binary mixtures largely independent of the mixture composition.
iDVIP: identification and characterization of viral integrase inhibitory peptides
Authors: Huang Kai-Yao , Kao Hui-Ju , Weng Tzu-Hsiang , Chen Chia-Hung , Weng Shun-Long 
Published: October 2022

AbstractAntiretroviral peptides are a kind of bioactive peptides that present inhibitory activity against retroviruses through various mechanisms. Among them, viral integrase inhibitory peptides (VINIPs) are a class of antiretroviral peptides that have the ability to block the action of integrase proteins, which is essential for retroviral replication. As the number of experimentally verified bioactive peptides has increased significantly, the lack of in silico machine learning approaches can effectively predict the peptides with the integrase inhibitory activity. Here, we have developed the first prediction model for identifying the novel VINIPs using the sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by 5-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.82%, a specificity of 88.81%, an accuracy of 88.37%, a balanced accuracy of 87.32% and a Matthews correlation coefficient value of 0.64. Most importantly, the model also consistently provides effective performance in independent testing. To sum up, we propose the first computational approach for identifying and characterizing the VINIPs, which can be considered novel antiretroviral therapy agents. Ultimately, to facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed, which is now freely available at http://mer.hc.mmh.org.tw/iDVIP/.
Bridging wounds: tissue adhesives’ essential mechanisms, synthesis and characterization, bioinspired adhesives and future perspectives
Authors: Xu Kaige , Wu Xiaozhuo , Zhang Xingying , Xing Malcolm 
Published: October 2022

Abstract
Bioadhesives act as a bridge in wound closure by forming an effective interface to protect against liquid and gas leakage and aid the stoppage of bleeding. To their credit, tissue adhesives have made an indelible impact on almost all wound-related surgeries. Their unique properties include minimal damage to tissues, low chance of infection, ease of use and short wound-closure time. In contrast, classic closures, like suturing and stapling, exhibit potential additional complications with long operation times and undesirable inflammatory responses. Although tremendous progress has been made in the development of tissue adhesives, they are not yet ideal. Therefore, highlighting and summarizing existing adhesive designs and synthesis, and comparing the different products will contribute to future development. This review first provides a summary of current commercial traditional tissue adhesives. Then, based on adhesion interaction mechanisms, the tissue adhesives are categorized into three main types: adhesive patches that bind molecularly with tissue, tissue-stitching adhesives based on pre-polymer or precursor solutions, and bioinspired or biomimetic tissue adhesives. Their specific adhesion mechanisms, properties and related applications are discussed. The adhesion mechanisms of commercial traditional adhesives as well as their limitations and shortcomings are also reviewed. Finally, we also discuss the future perspectives of tissue adhesives.
Disentangling the association between alcohol consumption and employment status: causation, selection or confounding?
Authors: Mangot-Sala Lluís , Smidt Nynke , Liefbroer Aart C 
Published: October 2022

Abstract

Background
Alcohol use constitutes a major health risk and is related to unemployment. However, the direction of this relationship is unclear: unemployment may change drinking patterns (causation), but heavy drinkers may also be more prone to lose their job (selection). We simultaneously examined selection and causation, and assessed the role of residual confounding. Moreover, we paid attention to the subgroup of abstainers and occupationally disabled, often disregarded in the literature.

Methods
Longitudinal data (three waves collected between 2006 and 2018) of the Lifelines Cohort study from the Netherlands were used (138 875 observations of 55 415 individuals, aged 18–60 at baseline). Alcohol use was categorized as ‘abstaining’, ‘moderate drinking’ and ‘binge drinking’ (≥5 drinks/occasion for male; ≥4 for women). Employment status included occupational disability, short (&lt;6 months) and long-term (≥6 months) unemployment. Random- and fixed-effects multinomial regression models were fitted in order to account for residual confounding. Reciprocal causality was assessed through generalized structural equation modelling with fixed-effects.

Results
Long unemployment spells increase the risk for both binge drinking (β = 0.23; 95% CI 0.04–0.42) and abstinence (β = 0.27; 95% CI 0.11–0.44), and the effects hold after accounting for reciprocal causality and time-constant confounding. Contrarily, the effect of binge drinking on unemployment is weak (β = 0.14; 95% CI −0.03 to 0.31). Abstinence is strongly associated with occupational disability (β = 0.40; 95% CI 0.24–0.57).

Conclusions
We find evidence supporting the causation hypothesis (unemployment altering drinking patterns), whereas evidence for the selection hypothesis is weak and mostly confounded by unobserved variables, such as poor health prior to baseline.
Pregnancy and Crohn’s disease: concerns and assurance of medical therapy
Authors: Chowdhury Reezwana , Kane Sunanda V 
Published: October 2022

Abstract
Approximately 50% of patients with inflammatory bowel disease including both Crohn’s disease and ulcerative colitis are female with many being diagnosed and treated during their reproductive years. It is important for women to be in remission prior to and during pregnancy. There have been many advances in the treatment of inflammatory bowel disease, including new therapies. In this review, we summarize the currently approved medications for Crohn’s disease and their safety in pregnancy and postpartum. The totality of evidence suggests that the majority of therapies are low-risk before and during pregnancy, and should be continued to control maternal disease.