In a world dominated by large-scale numerical simulations and applications of machine learning to biology, medicine and neuroscience (and complex systems, in general), each driven by the availability of 'big data' and decreasing costs of computation, the art form of constructing simple models is no longer in the focus of scientific attention. Yet, the most successful of these 'minimal models' have brought about a revolution of our understanding of complex systems, in particular in the life sciences, and are still shaping our view of the world today. It is hard to look at a power-law distribution without thinking of the Bak-Tang-Wiesenfeld sandpile model. And it is near impossible to reflect on synchronization without imagining coupled phase oscillators. Here we have put together a few ideas on how to formulate and analyze simple models and to draw conclusions from them.

Anastomosis is a critical surgical procedure for reconnecting the tissue ends. Despite progress in surgical techniques, complications such as leaks and strictures still pose serious health risks. Most traditional approaches primarily focus on tissue alignment and perfusion. However, they often overlook the influence of mechanical cues on healing process. In this review, we introduce the concept of mechanomedicine that is a new field for integration of biomechanics and mechanobiology to guide cellular responses and tissue regeneration during anastomosis. We summarize the mechanical characteristics of different anastomotic techniques, including the type and level of mechanical forces generated at the repair site. In addition, we explore how these mechanical cues affect signaling pathways, such as Piezo1, TRPV4, and YAP/TAZ that are essential for hemostasis, inflammation, and tissue regeneration during the anastomotic healing process. By evaluating conventional and emerging anastomotic techniques, we reveal how distinct mechanical cues influence clinical outcomes. Finally, we highlight the potential of artificial intelligence and robotic systems that may help to optimize the mechanical microenvironment, predict complications, and personalize treatments. This review will establishe a novel mechanomedicine-driven framework for anastomosis, aiming to utilize biomechanics and mechanobiology for enhancing surgical precision and improving patient outcomes.

A phylogenetic split ∼7.5 Ma (million years ago) separated paninan ancestors that were unlike today's chimpanzees, from homininan ancestors that were unlike Homo sapiens today; neither had evolved into their modern physical and behavioural forms. Those paninans gave rise to the mainly frugivorous woodland-dwelling chimpanzees (Pan troglodytes), whose multifemale-multimale troops have social hierarchies where prominent parts are played by promiscuous males whose female offspring have little choice after menarche but to seek sexual partners in other troops, hostility between troops notwithstanding, whilst male promiscuity is incompatible with paternal interest in their offspring, interest being provided mainly by mothers or female alloparents. Contrary to widespread conjecture that the social arrangements of Pan were those of primaeval homininans, it is proposed here that ∼4 Ma the nature of the mosaic landscapes (of grasslands and stands of trees) that were the habitat of australopithecine homininans, had 4 consequences that impinged on homininan evolution, differentiating it from that of woodland-dwelling paninans: (1) The diversity of whatever was available to eat was not the same in adjoining habitats of homininan social units, each of which may have been constrained by whatever mostly could be foraged, scavenged, eaten, or carried away, within perhaps a 2-hour walk; (2) Whatever was forageable, scavengeable, and edible within that distance likely was limited at any period of the year, so social units were increasingly omnivorous and necessarily small; (3) Smallness demanded cognitive ingenuity and transmissibility of existential information acquired by active inference generated by self-evidencing through enacted neuroethological behavioural responses, in line with the free energy principle, thanks to the cognitive broadening of homininan "zones of bounded surprisal" (ZBS) with respect to paninans' ZBS, both within each homininan "small-world" social unit and between nearby homininan units spreading out, in space and time, as budding very small-world information networks; (4) The existential continuity of small homininan social units depended on cooperation and sporadic collaboration between social units with mixed-sex philopatry (perhaps present ∼4 Ma among Australopithecus anamensis), behaviour which, together with (a) the generation of information within each unit that is enhanced by the intimate proximity to toddlers and children of older females and males in small mixed-sex social units, and (b) mixed-sex dispersal of sexually-active partners establishing mixed-sex social units at newly-formed localities nearby, was behaviour that maintained not only heterozygosity, but also, crucial cognitive awareness of kinship links favouring transmissibility of information and cooperation and collaboration (rather than hostility) between neighbouring social units, and was behaviour that represented evolutionary cognitive and social divergence from paninans. The vulnerability of small fragile social units implies there were hundreds of false dawns between ∼4 Ma and ∼40,000 BCE when even Homo neanderthalensis had vanished, leaving only our prehistoric Homo sapiens ancestors bearing "Homo informatio's" highly-evolved hierarchically mechanistic mind with its unequalled wide cognitive "zone of bounded surprisal" (ZBS) grounded in active inference in accord with the free energy principle.

Protein post-translational modifications (PTMs), which involve the covalent attachment of specific chemical groups to amino acid residues, significantly reshape protein structure and function. These modifications play a crucial role in fundamental physiological processes such as signal transduction, metabolic regulation, and protein homeostasis. In the context of pan-cancer, various types of PTMs, including phosphorylation, acetylation, glycosylation, and ubiquitination, create an intricate crosstalk network that finely tunes the stability and function of immune checkpoint molecules, directly influencing tumor immune evasion and immune cell recognition. Additionally, PTMs exert multilayered regulation over the functional states of key immune cells, such as T cells, macrophages, and dendritic cells (DCs), thereby determining the intensity and nature of immune responses within the tumor microenvironment (TME). Furthermore, PTMs are pivotal in antigen processing and presentation by influencing antigen diversity and epitope display, which facilitates tumor cell escape from immune surveillance. Dynamic analyses reveal that PTM landscapes exhibit spatiotemporal specificity during tumor initiation, progression, and metastasis, closely correlating with tumor stage and the establishment of an immunosuppressive microenvironment. Based on these findings, immunotherapeutic strategies targeting key PTM-modifying enzymes, such as kinases, deacetylases, and deubiquitinases, are rapidly emerging. However, these approaches still face challenges, including drug specificity, resistance, and off-target effects. The exploration of synergistic effects through the combinational targeting of distinct PTM pathways, along with a deeper understanding of the interactive regulatory networks among PTMs, opens promising avenues for the development of next-generation precision immunotherapies. This review aims to systematically elucidate the multifaceted roles and dynamic regulation of PTMs in tumor immunity, providing a theoretical foundation and research direction for identifying novel immunotherapeutic targets and optimizing therapeutic strategies.

Recent discussions about the nature of meaning and concepts focus on abstract semantic knowledge including key information about inner states of the individual. Classic cognitive approaches anchor the meaning of words in universal concepts, semantic networks or semantic features encapsulated in the individual's own mind. However, this does not explain how symbols become interpretable during language development. Embodiment theorists acknowledge the relevance of semantic grounding of concrete referential symbols in perceptions and actions during learning, but, similar to classic cognitivism, assume internal anchoring of mental terms in introspection, thus once again implicating a main role of privileged access to 'private' inner states in language learning. This raises the basic question as to how a public language can be founded in private inner access. Here, we argue that, in semantic learning, a purely introspection-based classification of inner states is neither possible nor required, and even less so a first-person privilege in accessing these states. Rather, classification and semantic learning of symbols for mentalistic concepts is an interactive process between the learner and an external observer who can employ contextual knowledge and behavioral information for recognizing and categorizing the learner's mental states. In support of this 'extrospective' mental grounding account, we review observations that, in case of doubt about internal states, third-person evidence can play a decisive role. We also highlight supporting empirical studies showing that individuals in whom the link between first-person experience and externally observable behavior is broken may suffer from deficits in processing and understanding the related mentalistic vocabulary.

Understanding and promoting cooperative behaviour among self-interested individuals is a critical concern in physical, biological, and social sciences. Numerous foundational mechanisms for the evolution of cooperation have been identified, and these mechanisms have served as the basis for developing tools and interventions designed to sustain and enhance cooperative behaviour. However, since both foundational mechanisms and the derived tools and interventions often involve costs affecting individuals or institutions, striving for maximum cooperation can sometimes harm social welfare, defined as the total population payoff. Herein, we review existing evolutionary mechanisms for the evolution of cooperation as well as tools and interventions based on these mechanisms, emphasising the often-overlooked hidden costs that may lead to a misalignment between cooperation and social welfare. By explicitly incorporating these hidden factors into the models, we analyse the conditions under which they reduce social welfare, across a broad range of social dilemma games and evolutionary forces. Additionally, we review experimental studies that support and inform mathematical models and agent-based simulations. We highlight when considering social welfare is crucial, as misalignment is most likely to occur. Ultimately, we argue that social welfare, not just cooperation, should be the primary optimisation objective when designing interventions for social good. We also suggest several key directions to further explore this often-overlooked issue in the literature. Overall, we reveal that hidden costs often influence the alignment between cooperation and social welfare, challenging the common prioritisation of cooperation alone.

The multifaceted nature of subjective experience poses a challenge to the study of consciousness. Traditional neuroscientific approaches often concentrate on isolated facets, such as perceptual awareness or the global state of consciousness and construct a theory around the relevant empirical paradigms and findings. Theories of consciousness are, therefore, often difficult to compare; indeed, there might be little overlap in the phenomena such theories aim to explain. Here, we take a different approach: starting with active inference, a first principles framework for modelling behaviour as (approximate) Bayesian inference, and building up to a minimal theory of consciousness, which emerges from the shared features of computational models derived under active inference. We review a body of work applying active inference models to the study of consciousness and argue that there is implicit in all these models a small set of theoretical commitments that point to a minimal (and testable) theory of consciousness.