Mobile Genetic Element Flexibility as an Underlying Principle to Bacterial Evolution.

Mobile genetic elements are key to the evolution of bacteria and traits that affect host and ecosystem health. Here, we use a framework of a hierarchical and modular system that scales from genes to populations to synthesize recent findings on mobile genetic elements (MGEs) of bacteria. Doing so highlights the role that emergent properties of flexibility, robustness, and genetic capacitance of MGEs have on the evolution of bacteria. Some of their traits can be stored, shared, and diversified across different MGEs, taxa of bacteria, and time. Collectively, these properties contribute to maintaining functionality against perturbations while allowing changes to accumulate in order to diversify and give rise to new traits. These properties of MGEs have long challenged our abilities to study them. Implementation of new technologies and strategies allows for MGEs to be analyzed in new and powerful ways.

Congruence modules in higher codimension and zeta lines in Galois cohomology.

This article builds on recent work of the first three authors where a notion of congruence modules in higher codimension is introduced. The main results are a criterion for detecting regularity of local rings in terms of congruence modules, and a more refined version of a result tracking the change of congruence modules under deformation. Number theoretic applications include the construction of canonical lines in certain Galois cohomology groups arising from adjoint motives of Hilbert modular forms.

Functional modules for visual scene segmentation in macaque visual cortex.

Segmentation, the computation of object boundaries, is one of the most important steps in intermediate visual processing. Previous studies have reported cells across visual cortex that are modulated by segmentation features, but the functional role of these cells remains unclear. First, it is unclear whether these cells encode segmentation consistently since most studies used only a limited variety of stimulus types. Second, it is unclear whether these cells are organized into specialized modules or instead randomly scattered across the visual cortex: the former would lend credence to a functional role for putative segmentation cells. Here, we used fMRI-guided electrophysiology to systematically characterize the consistency and spatial organization of segmentation-encoding cells across the visual cortex. Using fMRI, we identified a set of patches in V2, V3, V3A, V4, and V4A that were more active for stimuli containing figures compared to ground, regardless of whether figures were defined by texture, motion, luminance, or disparity. We targeted these patches for single-unit recordings and found that cells inside segmentation patches were tuned to both figure-ground and borders more consistently across types of stimuli than cells in the visual cortex outside the patches. Remarkably, we found clusters of cells inside segmentation patches that showed the same border-ownership preference across all stimulus types. Finally, using a population decoding approach, we found that segmentation could be decoded with higher accuracy from segmentation patches than from either color-selective or control regions. Overall, our results suggest that segmentation signals are preferentially encoded in spatially discrete patches.

Having multiple selves helps learning agents explore and adapt in complex changing worlds.

Satisfying a variety of conflicting needs in a changing environment is a fundamental challenge for any adaptive agent. Here, we show that designing an agent in a modular fashion as a collection of subagents, each dedicated to a separate need, powerfully enhanced the agent's capacity to satisfy its overall needs. We used the formalism of deep reinforcement learning to investigate a biologically relevant multiobjective task: continually maintaining homeostasis of a set of physiologic variables. We then conducted simulations in a variety of environments and compared how modular agents performed relative to standard monolithic agents (i.e., agents that aimed to satisfy all needs in an integrated manner using a single aggregate measure of success). Simulations revealed that modular agents a) exhibited a form of exploration that was intrinsic and emergent rather than extrinsically imposed; b) were robust to changes in nonstationary environments, and c) scaled gracefully in their ability to maintain homeostasis as the number of conflicting objectives increased. Supporting analysis suggested that the robustness to changing environments and increasing numbers of needs were due to intrinsic exploration and efficiency of representation afforded by the modular architecture. These results suggest that the normative principles by which agents have adapted to complex changing environments may also explain why humans have long been described as consisting of "multiple selves."

Untangling the relationship between developmental and evolutionary integration.

Patterns of integration and modularity among organismal traits are prevalent across the tree of life, and at multiple scales of biological organization. Over the past several decades, researchers have studied these patterns at the developmental, and evolutionary levels. While their work has identified the potential drivers of these patterns at different scales, there appears to be a lack of consensus on the relationship between developmental and evolutionary integration. Here, we review and summarize key studies and build a framework to describe the conceptual relationship between these patterns across organismal scales and illustrate how, and why some of these studies may have yielded seemingly conflicting outcomes. We find that among studies that analyze patterns of integration and modularity using morphological data, the lack of consensus may stem in part from the difficulty of fully disentangling the developmental and functional causes of integration. Nonetheless, in some empirical systems, patterns of evolutionary modularity have been found to coincide with expectations based on developmental processes, suggesting that in some circumstances, developmental modularity may translate to evolutionary modularity. We also advance an extension to Hallgrímsson et al.'s palimpsest model to describe how patterns of trait modularity may shift across different evolutionary scales. Finally, we also propose some directions for future research which will hopefully be useful for investigators interested in these issues.

Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization.

Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

Structural Modularity Tunes Mesoscale Criticality in Biological Neuronal Networks.

Numerous studies suggest that biological neuronal networks self-organize toward a critical state with stable recruitment dynamics. Individual neurons would then statistically activate exactly one further neuron during activity cascades termed neuronal avalanches. Yet, it is unclear if and how this can be reconciled with the explosive recruitment dynamics within neocortical minicolumns in vivo and within neuronal clusters in vitro, which indicates that neurons form supercritical local circuits. Theoretical studies propose that modular networks with a mix of regionally subcritical and supercritical dynamics would create apparently critical dynamics, resolving this inconsistency. Here, we provide experimental support by manipulating the structural self-organization process of networks of cultured rat cortical neurons (either sex). Consistent with the prediction, we show that increasing clustering in neuronal networks developing in vitro strongly correlates with avalanche size distributions transitioning from supercritical to subcritical activity dynamics. Avalanche size distributions approximated a power law in moderately clustered networks, indicating overall critical recruitment. We propose that activity-dependent self-organization can tune inherently supercritical networks toward mesoscale criticality by creating a modular structure in neuronal networks.SIGNIFICANCE STATEMENT Critical recruitment dynamics in neuronal networks are considered optimal for information processing in the brain. However, it remains heavily debated how neuronal networks would self-organize criticality by detailed fine-tuning of connectivity, inhibition, and excitability. We provide experimental support for theoretical considerations that modularity tunes critical recruitment dynamics at the mesoscale level of interacting neuron clusters. This reconciles reports of supercritical recruitment dynamics in local neuron clusters with findings on criticality sampled at mesoscopic network scales. Intriguingly, altered mesoscale organization is a prominent aspect of various neuropathological diseases currently investigated in the framework of criticality. We therefore believe that our findings would also be of interest for clinical scientists searching to link the functional and anatomic signatures of such brain disorders.

The Hierarchical Coevolutionary Units of Ecological Networks.

In ecological networks, cohesive groups of species may shape the evolution of interactions, serving as coevolutionary units. Ranging across network scales, from motifs to isolated components, elucidating which cohesive groups are more determinant for coevolution remains a challenge in ecology. We address this challenge by integrating 376 empirical mutualistic and antagonistic networks and coevolutionary models. We identified cohesive groups at four network scales containing a significant proportion of potential direct coevolutionary effects. Cohesive groups displayed hierarchical organisation, and potential coevolutionary effects overflowing lower-scale groups were contained by higher-scale groups, underscoring the hierarchy's impact. However, indirect coevolutionary effects blurred group boundaries and hierarchy, particularly under strong selection from ecological interactions. Thus, under strong selection, indirect effects render networks themselves, and not cohesive groups, as the likely coevolutionary units of ecological systems. We hypothesise hierarchical cohesive groups to also shape how other forms of direct and indirect effects propagate in ecological systems.

The dimensionality of infection networks among viruses infecting microbial eukaryotes and bacteria.

Diverse viruses and their hosts are interconnected through complex networks of infection, which are thought to influence ecological and evolutionary processes, but the principles underlying infection network structure are not well understood. Here we focus on network dimensionality and how it varies across 37 networks of viruses infecting eukaryotic phytoplankton and bacteria. We find that dimensionality is often strikingly low, with most networks being one- or two-dimensional, although dimensionality increases with network richness, suggesting that the true dimensionality of natural systems is higher. Low-dimensional networks generally exhibit a mixture of host partitioning among viruses and nestededness of host ranges. Networks of bacteria-infecting and eukaryote-infecting viruses possess comparable distributions of dimensionality and prevalence of nestedness, indicating that fundamentals of network structure are similar among domains of life and different viral lineages. The relative simplicity of many infection networks suggests that coevolutionary dynamics are often driven by a modest number of underlying mechanisms.

Dorsal and Ventral Plumage Coloration Evolve as Distinct Modules with Different Environmental Correlations.

AbstractMany animals exhibit contrast between their dorsal coloration and their ventral coloration. If selection acts differently on dorsal versus ventral coloration, ancestral covariance between these traits should break down, eventually leading to independent modules of trait evolution. Here, we compare the evolution of feather color across body regions for a clade of Australasian songbirds (Meliphagoidea). We find evidence for three modules of covarying color regions. Among these modules, ventral feathers evolve with high lability, evolving at three times the rate of dorsal plumage and 20 times the rate of flight feathers. While both dorsal plumage and ventral plumage are darker in areas with more precipitation and vegetation, we find that dorsal plumage is twice as similar to colors in satellite photos of background substrates. Overall, differential selection on ventral and dorsal colors likely maintains these as distinct modules over evolutionary timescales-a novel explanation for dorsoventral contrast in pigmentation.

Mechanistic interactions as the origin of modularity in biological networks.

Biological networks are often modular. Explanations for this peculiarity either assume an adaptive advantage of a modular design such as higher robustness, or attribute it to neutral factors such as constraints underlying network assembly. Interestingly, most insights on the origin of modularity stem from models in which interactions are either determined by highly simplistic mechanisms, or have no mechanistic basis at all. Yet, empirical knowledge suggests that biological interactions are often mediated by complex structural or behavioural traits. Here, we investigate the origins of modularity using a model in which interactions are determined by potentially complex traits. Specifically, we model system elements-such as the species in an ecosystem-as finite-state machines (FSMs), and determine their interactions by means of communication between the corresponding FSMs. Using this model, we show that modularity probably emerges for free. We further find that the more modular an interaction network is, the less complex are the traits that mediate the interactions. Altogether, our results suggest that the conditions for modularity to evolve may be much broader than previously thought.

Phenotypic and genomic dissection of colour pattern variation in a reef fish radiation.

Coral reefs rank among the most diverse species assemblages on Earth. A particularly striking aspect of coral reef communities is the variety of colour patterns displayed by reef fishes. Colour pattern is known to play a central role in the ecology and evolution of reef fishes through, for example, signalling or camouflage. Nevertheless, colour pattern is a complex trait in reef fishes-actually a collection of traits-that is difficult to analyse in a quantitative and standardized way. This is the challenge that we address in this study using the hamlets (Hypoplectrus spp., Serranidae) as a model system. Our approach involves a custom underwater camera system to take orientation- and size-standardized photographs in situ, colour correction, alignment of the fish images with a combination of landmarks and Bézier curves, and principal component analysis on the colour value of each pixel of each aligned fish. This approach identifies the major colour pattern elements that contribute to phenotypic variation in the group. Furthermore, we complement the image analysis with whole-genome sequencing to run a multivariate genome-wide association study for colour pattern variation. This second layer of analysis reveals sharp association peaks along the hamlet genome for each colour pattern element and allows to characterize the phenotypic effect of the single nucleotide polymorphisms that are most strongly associated with colour pattern variation at each association peak. Our results suggest that the diversity of colour patterns displayed by the hamlets is generated by a modular genomic and phenotypic architecture.

An inclusive anatomical network analysis of human craniocerebral topology.

The human brain's complex morphology is spatially constrained by numerous intrinsic and extrinsic physical interactions. Spatial constraints help to identify the source of morphological variability and can be investigated by employing anatomical network analysis. Here, a model of human craniocerebral topology is presented, based on the bony elements of the skull at birth and a previously designed model of the brain. The goal was to investigate the topological components fundamental to the craniocerebral geometric balance, to identify underlying phenotypic patterns of spatial arrangement, and to understand how these patterns might have influenced the evolution of human brain morphology. Analysis of the craniocerebral network model revealed that the combined structure of the body and lesser wings of the sphenoid bone, the parahippocampal gyrus, and the parietal and ethmoid bones are susceptible to sustain and apply major spatial constraints that are likely to limit or channel their morphological evolution. The results also showcase a high level of global integration and efficient diffusion of biomechanical forces across the craniocerebral system, a fundamental aspect of morphological variability in terms of plasticity. Finally, community detection in the craniocerebral system highlights the concurrence of a longitudinal and a vertical modular partition. The former reflects the distinct morphogenetic environments of the three endocranial fossae, while the latter corresponds to those of the basicranium and calvaria.

Graph analysis uncovers an opposing impact of methylphenidate on connectivity patterns within default mode network sub-divisions.

BACKGROUND: The Default Mode Network (DMN) is a central neural network, with recent evidence indicating that it is composed of functionally distinct sub-networks. Methylphenidate (MPH) administration has been shown before to modulate impulsive behavior, though it is not yet clear whether these effects relate to MPH-induced changes in DMN connectivity. To address this gap, we assessed the impact of MPH administration on functional connectivity patterns within and between distinct DMN sub-networks and tested putative relations to variability in sub-scales of impulsivity. METHODS: Fifty-five right-handed healthy adults underwent two resting-state functional MRI (rs-fMRI) scans, following acute administration of either MPH (20 mg) or placebo, via a randomized double-blind placebo-controlled design. Graph modularity analysis was implemented to fractionate the DMN into distinct sub-networks based on the impact of MPH (vs. placebo) on DMN connectivity patterns with other neural networks. RESULTS: MPH administration led to an overall decreased DMN connectivity, particularly with the auditory, cinguloopercular, and somatomotor networks, and increased connectivity with the parietomedial network. Graph analysis revealed that the DMN could be fractionated into two distinct sub-networks, with one exhibiting MPH-induced increased connectivity and the other decreased connectivity. Decreased connectivity of the DMN sub-network with the cinguloopercular network following MPH administration was associated with elevated impulsivity and non-planning impulsiveness. CONCLUSION: Current findings highlight the intricate effects of MPH administration on DMN rs-fMRI connectivity, uncovering its opposing impact on distinct DMN sub-divisions. MPH-induced dynamics in DMN connectivity patterns with other neural networks may account for some of the effects of MPH administration on impulsive behavior.

Revisiting the Modularity-Disease transmission Link: Uncovering the importance of intra-modular structure.

Studies have shown that the internal structure of modules is hardly important for the spread of epidemics. However, most of these studies have assumed that intra-module connectivity and inter-module connectivity do not affect each other. In reality, changes in the internal structure of modules may affect inter-module links and thus change the modularity of the entire network. Therefore, we have developed a theoretical network model with adjustable modularity to investigate the impact of this situation on disease transmission. Our findings indicate that the intra-module structure plays a crucial role in disease outbreaks. Changes in intra-module structure lead to significant numerical changes in peak prevalence and duration of disease. This implies that the potential impact of changes in exposure patterns within modules should also be considered when investigating the exact impact of modular social networks on disease burden.

Community connectivity and local heterogeneity explain animal species co-occurrences within pond communities.

Metacommunity processes have the potential to determine most features of the community structure. However, species diversity has been the dominant focus of studies. Nestedness, modularity and checkerboard distribution of species occurrences are main components of biodiversity organisation. Within communities, these patterns emerge from the interaction between functional diversity, spatial heterogeneity and resource availability. Additionally, the connectivity determines the pool of species for community assembly and, eventually, the pattern of species co-occurrence within communities. Despite the recognised theoretical expectations, the change in occurrence patterns within communities along ecological gradients has seldom been considered. Here, we analyse the spatial occurrence of animal species along sampling units within 18 temporary ponds and its relationship with pond environments and geographic isolation. Isolated ponds presented a nested organisation of species with low spatial segregation-modularity and checkerboard-and the opposite was found for communities with high connectivity. A pattern putatively explained by high functional diversity in ponds with large connectivity and heterogeneity, which determines that species composition tracks changes in microhabitats. On the contrary, nestedness is promoted in dispersal-limited communities with low functional diversity, where microhabitat filters mainly affect richness without spatial replacement between functional groups. Vegetation biomass promotes nestedness, probably due to the observed increase in spatial variance in biomass with the mean biomass. Similarly, the richness of vegetation reduced the spatial segregation of animals within communities. This result may be due to the high plant diversity of the pond that is observed similarly along all sampling units, which promotes the spatial co-occurrence of species at this scale. In the study system, the spatial arrangement of species within communities is related to local drivers as heterogeneity and metacommunity processes by means of dispersal between communities. Patterns of species co-occurrence are interrelated with community biodiversity and species interactions, and consequently with most functional and structural properties of communities. These results indicate that understanding the interplay between metacommunity processes and co-occurrence patterns is probably more important than previously thought to understand biodiversity assembly and functioning.

Los procesos metacomunitarios tienen el potencial de determinar la mayoría de las características de la estructura de las comunidades. Sin embargo, los trabajos se han enfocado principalmente en los patrones de diversidad de especies. El anidamiento, la modularidad y la distribución en damero de la ocurrencia espacial de las especies son propiedades básicas de las comunidades. Estos patrones surgen de la interacción entre la diversidad funcional, la heterogeneidad espacial y la disponibilidad de recursos dentro de las comunidades. Además, el pool de especies disponibles para el ensamblaje está determinado por la conectividad de la comunidad, afectando así su patrón de co­ocurrencia de especies. A pesar de las reconocidas expectativas teóricas, el cambio en los patrones de ocurrencia dentro de las comunidades a lo largo de gradientes ecológicos ha sido poco considerado. Aquí, analizamos la ocurrencia espacial de especies animales dentro de 18 charcos temporales y su relación con las características ambientales y el aislamiento geográfico de los charcos. Los charcos aislados presentaron alto anidamiento espacial mientras que los charcos de alta conectividad una distribución de ocurrencias modular y en damero. Por un lado, la baja diversidad funcional en charcos aislados, determinaría que los filtros microambientales afecten la riqueza de especies sin reemplazo espacial entre grupos funcionales, promoviendo un arreglo anidado de ocurrencias. Por otro lado, la alta diversidad funcional en charcos con alta conectividad y heterogeneidad permitiría el reemplazo espacial de especies en gradientes microambientales, determinando los patrones de segregación observados. La biomasa vegetal promueve el anidamiento, probablemente debido al aumento observado en la variación espacial de la biomasa con la biomasa media. La riqueza vegetal también redujo la segregación espacial de los animales dentro de las comunidades. Este resultado puede deberse a que la alta diversidad de plantas de los charcos es también observada a nivel de unidades muestreales, favoreciendo esto la coexistencia espacial de especies. El arreglo espacial de especies dentro de las comunidades estudiadas estaría determinado tanto por factores locales como la heterogeneidad, como por procesos regionales operando a través de la dispersión de individuos entre comunidades. Los patrones de co­ocurrencia de especies están interrelacionados con la diversidad comunitaria y las interacciones bióticas, y consecuentemente con la mayoría de las propiedades estructurales y funcionales de las comunidades. Este estudio evidencia la importancia de la conexión entre procesos metacomunitarios y la co­ocurrencia espacial de especies para comprender el ensamblaje y funcionamiento de la biodiversidad.

A network model of legal relations.

From at least the early twentieth century, legal scholars have recognized that rights and other legal relations inhere between individual legal actors, forming a vast and complex social network. Yet, no legal scholar has used the mathematical machinery of network theory to formalize these relationships. Here, we propose the first such approach by modelling a rudimentary, static set of real property relations using network theory. Then, we apply our toy model to measure the level of modularity-essentially, the community structure-among aggregations of these real property relations and associated actors. In so doing, we show that even for a very basic set of relations and actors, law may employ modular structures to manage complexity. Property, torts, contracts, intellectual property, and other areas of the law arguably reduce information costs in similar, quantifiable ways by chopping up the world of interactions between parties into manageable modules that are semi-autonomous. We also posit that our network science approach to jurisprudential issues can be adapted to quantify many other important aspects of legal systems. This article is part of the theme issue 'A complexity science approach to law and governance'.

Emotional states as distinct configurations of functional brain networks.

The conceptualization of emotional states as patterns of interactions between large-scale brain networks has recently gained support. Yet, few studies have directly examined the brain's network structure during emotional experiences. Here, we investigated the brain's functional network organization during experiences of sadness, amusement, and neutral states elicited by movies, in addition to a resting-state. We tested the effects of the experienced emotion on individual variability in the brain's functional connectome. Next, for each state, we defined a community structure of the brain and quantified its segregation and integration. We found that sadness, relative to amusement, was associated with higher modular integration and increased connectivity of cognitive control networks: the salience and fronto-parietal networks. Moreover, in both the functional connectome and the emotional report, the similarity between individuals was dependent on the sex. Our results suggest that the experience of emotion is linked to a reconfiguration of whole-brain distributed, not emotion-specific, functional networks and that the brain's topological structure carries information about the subjective emotional experience.

Whole-brain dynamics of human sensorimotor adaptation.

Humans vary greatly in their motor learning abilities, yet little is known about the neural processes that underlie this variability. We identified distinct profiles of human sensorimotor adaptation that emerged across 2 days of learning, linking these profiles to the dynamics of whole-brain functional networks early on the first day when cognitive strategies toward sensorimotor adaptation are believed to be most prominent. During early learning, greater recruitment of a network of higher-order brain regions, involving prefrontal and anterior temporal cortex, was associated with faster learning. At the same time, greater integration of this "cognitive network" with a sensorimotor network was associated with slower learning, consistent with the notion that cognitive strategies toward adaptation operate in parallel with implicit learning processes of the sensorimotor system. On the second day, greater recruitment of a network that included the hippocampus was associated with faster learning, consistent with the notion that declarative memory systems are involved with fast relearning of sensorimotor mappings. Together, these findings provide novel evidence for the role of higher-order brain systems in driving variability in adaptation.

Global brain network modularity dynamics after local optic nerve damage following noninvasive brain stimulation: an EEG-tracking study.

Tightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities ("modules") during visual processing and if and how this modularity changes postlesion (progression or recovery) following neuromodulation. Using the damaged optic nerve as a paradigm, we now studied brain FCN modularity dynamics to better understand module interactions and dynamic reconfigurations before and after neuromodulation with noninvasive repetitive transorbital alternating current stimulation (rtACS). We found that in both patients and controls, local intermodule interactions correlated with visual performance. However, patients' recovery of vision after treatment with rtACS was associated with improved interaction strength of pathways linked to the attention module, and it improved global modularity and increased the stability of FCN. Our results show that temporal coordination of multiple cortical modules and intermodule interaction are functionally relevant for visual processing. This modularity can be neuromodulated with tACS, which induces a more optimal balanced and stable multilayer modular structure for visual processing by enhancing the interaction of neural pathways with the attention network module.