BIOND LAB

A key unresolved question in microbial ecology is how the extraordinary diversity of microbiomes emerges from the interactions among their many functionally distinct populations. This process is driven in part by the cross-feeding networks that help to structure these systems, in which consumers use resources to fuel their metabolism, creating by-products which can be used by others in the community. Understanding the effects of cross-feeding presents a major challenge, as it creates complex interdependencies between populations which can be hard to untangle. We address this problem using the tools of network science to develop a structural microbial community model. Using methods from percolation theory, we identify feasible community states for cross-feeding network structures in which the needs of consumers are met by metabolite production across the community. We identify tipping points at which small changes in structure can cause the catastrophic collapse of cross-feeding networks and abrupt declines in microbial community diversity. Our results are an example of a well-defined tipping point in a complex ecological system and provide insight into the fundamental processes shaping microbiomes and their robustness. We further demonstrate this by considering how network attacks affect community diversity and apply our results to show how the apparent difficulty in culturing the microbial diversity emerges as an inherent property of their cross-feeding networks.

In adaptive dynamical networks, the dynamics of the nodes and the edges influence each other. We show that we can treat such systems as a closed feedback loop between edge and node dynamics. Using recent advances on the stability of feedback systems from control theory, we derive local, sufficient conditions for steady states of such systems to be linearly stable. These conditions are local in the sense that they are written entirely in terms of the (linearized) behavior of the edges and nodes. We apply these conditions to the Kuramoto model with inertia written in adaptive form, and the adaptive Kuramoto model. For the former we recover a classic result, for the latter we show that our sufficient conditions match necessary conditions where the latter are available, thus completely settling the question of linear stability in this setting. The method we introduce can be readily applied to a vast class of systems. It enables straightforward evaluation of stability in highly heterogeneous systems.

Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.

Ships are technologies of maritime mobility. But sometimes ships are immobile—they stop and remain stationary for short or prolonged times. A degree of stasis inside and outside ports is both usual and essential for facilitating the movement of ships in global markets. This paper makes two important points: first, echoing existing literature, frequent stationarity (or waiting) is a normal occurrence in the industry. Second, it is not just mobilities that are disrupted via moments of stasis; immobilities themselves have distinct patterns that too can be disrupted. This nuance is vital: there is a need to understand the disruptions to immobilities rather than understanding immobilities as disruptions to the general condition of mobility, both within—and beyond—the shipping example. We argue that understanding disruptions to immobilities is vital to grasping the dynamics of shipping, alongside other (im)mobilites (car, train, plane), their conditions, and the politics that shape our world on the move. Using a data-driven approach, embracing AIS methods for exploring ship stationarity around US waters during the COVID-19 pandemic, this paper upends the assumptions of ship (im)mobilities through the example of wait times, calling for scholars to give space and time to everyday immobilities and their disruptions too.

Responding to the dearth of fully-comprehensive or summary data on prisoner or ‘carceral mobilities’, this paper provides the first comprehensive case study analysis of the flow into, between and out from prisons. By uniquely extracting data from the 2011 UK Census to identify and visualise trends in movement, highlight centrality of institutions and observe the self-containedness of regions of operation, findings reveal specific volumes and geographies of prisoner flow as well as discrepancies with the expected practices of prison category transfers and disparities between the distances travelled by prisoners in establishments with different functions. Such analysis is a critical tool in appraising (in)efficiencies with the governance of prisons at the regional and national level. In conclusion, Census Data is revealed as a viable source of data for analysis in situations where institutional data is not forthcoming/available, which provides significant potential for the advancement of the range and scope of studies in carceral mobilities and criminological research more broadly.

This paper positions possibilities for human geographies of the sea. The growing volume of work under this banner has been largely qualitative in its approach, reflecting, in turn, the questions posed by oceanic scholars. These questions necessitate corresponding methods. Whilst this is not necessarily a problem, and the current corpus of work has offered many significant contributions, in making sense of the human dimensions of maritime worlds, other questions —and methods—may generate knowledge that is useful within this remit of work. This paper considers the place of quantitative approaches in posing lines of enquiry about shipping, one of the prominent areas of concern under the banner of ‘human geographies of the seas’. There is longstanding work in transport geographies concerned with shipping, logistics, freight movement and global connections, which embraces quantitative methods which could be bridged to ask fresh questions about oceanic spatial phenomena past and present. This paper reviews the state of the art of human geographies of the sea and transport geographies and navigates how the former field may be stimulated by some of the interests of the latter and a broader range of questions about society‐sea‐space relations. The paper focuses on Automatic Identification Systems (or AIS) as a potentially useful tool for connecting debates, and deepening spatial understandings of the seas and shipping beyond current scholarship. To advance the argument the example of shipping layups is used to illustrate or rather, position, the point.

Traditionally urban structure and development are monitored using infrequent high-quality datasets such as censuses. However, human culture is accelerating and aggregating, leading to ever-larger cities and an increased pace of urban development. Our modern interconnected world also provides us with new data sources that can be leveraged in the study of cities. However, these often noisy and unstructured sources of big data pose new challenges. Here we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which is produced as a byproduct of mobile communication, we show that meaningful features can be extracted, revealing for example the emergence and absorption of subcenters. In the future this method will allow the analysis of urban dynamics at a high spatial resolution (here, 500m) and near real-time frequency.

Many of the global challenges that confront humanity are interlinked in a dynamic complex network, with multiple feedback loops, nonlinear interactions and interdependencies that make it difficult, if not impossible, to consider individual threats in isolation. These challenges are mainly dealt with, however, by considering individual threats in isolation (at least in political terms). The mitigation of dual climate and biodiversity threats, for example, is linked to a univariate 1.5°C global warming boundary and a global area conservation target of 30% by 2030. The situation has been somewhat improved by efforts to account for interactions through multidimensional target setting, adaptive and open management and market-based decision pathways. But the fundamental problem still remains—that complex systems such as those formed by the network of global threats have emergent properties that are more than the sum of their parts. We must learn how to deal with or live with these properties if we are to find effective ways to cope with the threats, individually and collectively. Here, we argue that recent progresses in complex systems research and related fields have enhanced our ability to analyse and model such entwined systems to the extent that it offers the promise of a new approach to sustainability. We discuss how this may be achieved, both in theory and in practice, and how human cultural factors play an important but neglected role that could prove vital to achieving success.

By-catch. A noun. British English. “Unwanted fish and other sea animals caught in a fishing net along with the desired kind of fish.” Bycatch. Scientific definition. “Bycatch refers most often to those species incidentally taken in fishing operations aimed at other (target) species... [it] refers to species accidentally caught other than the target species, brought on board, dead or alive, and that can therefore be either released alive, discarded dead, or landed.” What happens when you spin a net out into a sea of data? What is returned? What is valuable (what is not)? What is by-catch (or bycatch)? What is unwanted, alongside that which is wanted? What is unintentional, caught by accident? And what should be discarded (or thrown back into the metaphorical waters)? What is kept? Landed. Why?