Connectivity and complex systems: learning from a multi-disciplinary perspective

Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction

Introduction

Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction.

Methods

By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy.

Results

By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset.

Conclusion

In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.

Biodiversity as a firewall to engineered microbiomes for restoration and conservation

The possibility of abrupt transitions threatens to poise ecosystems into irreversibly degraded states. Synthetic biology has recently been proposed to prevent them from crossing tipping points. However, there is little understanding of the impact of such intervention on the resident communities. Can such modification have 'unintended consequences', such as loss of species? Here, we address this problem by using a mathematical model that allows us to simulate this intervention scenario explicitly. We show how the indirect effect of damping the decay of shared resources results in biodiversity increase, and last but not least, the successful incorporation of the synthetic within the ecological network and very small-positive changes in the population size of the resident community. Furthermore, extensions and implications for future restoration and terraformation strategies are discussed.

Climate change projected to impact structural hillslope connectivity at the global scale

Structural connectivity describes how landscapes facilitate the transfer of matter and plays a critical role in the flux of water, solutes, and sediment across the Earth's surface. The strength of a landscape's connectivity is a function of climatic and tectonic processes, but the importance of these drivers is poorly understood, particularly in the context of climate change. Here, we provide global estimates of structural connectivity at the hillslope level and develop a model to describe connectivity accounting for tectonic and climate processes. We find that connectivity is primarily controlled by tectonics, with climate as a second order control. However, we show climate change is projected to alter global-scale connectivity at the end of the century (2070 to 2100) by up to 4% for increasing greenhouse gas emission scenarios. Notably, the Ganges River, the world's most populated basin, is projected to experience a large increase in connectivity. Conversely, the Amazon River and the Pacific coast of Patagonia are projected to experience the largest decreases in connectivity. Modeling suggests that, as the climate warms, it could lead to increased erosion in source areas, while decreased rainfall may hinder sediment flow downstream, affecting landscape connectivity with implications for human and environmental health.

Using compartmental models and Particle Swarm Optimization to assess Dengue basic reproduction number R 0 for the Republic of Panama in the 1999-2022 period

Nowadays, the ability to make data-driven decisions in public health is of utmost importance. To achieve this, it is necessary for modelers to comprehend the impact of models on the future state of healthcare systems. Compartmental models are a valuable tool for making informed epidemiological decisions, and the proper parameterization of these models is crucial for analyzing epidemiological events. This work evaluated the use of compartmental models in conjunction with Particle Swarm Optimization (PSO) to determine optimal solutions and understand the dynamics of Dengue epidemics. The focus was on calculating and evaluating the rate of case reproduction, R 0 , for the Republic of Panama. Three compartmental models were compared: Susceptible-Infected-Recovered (SIR), Susceptible-Exposed-Infected-Recovered (SEIR), and Susceptible-Infected-Recovered Human-Susceptible-Infected Vector (SIR Human-SI Vector, SIR-SI). The models were informed by demographic data and Dengue incidence in the Republic of Panama between 1999 and 2022, and the susceptible population was analyzed. The SIR, SEIR, and SIR-SI models successfully provided R 0 estimates ranging from 1.09 to 1.74. This study provides, to the best of our understanding, the first calculation of R 0 for Dengue outbreaks in the Republic of Panama.

Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review

Background

Dementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification.

Objective

With the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues.

Methods

In this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy.

Results

Out of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics.

Significance

This study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.

Tingbjerg Changing Diabetes: experiencing and navigating complexity in a community-based health promotion initiative in a disadvantaged neighbourhood in Copenhagen, Denmark

As a response to the complexity of reducing health inequity there has been a rise in community-based health promotion interventions adhering to the principles of complexity thinking. Such interventions often work with adaptive practice and constitute themselves in complex webs of collaborations between multiple stakeholders. However, few efforts have been made to articulate how complexity can be navigated and addressed by stakeholders in practice. This study explores how partners experience and navigate complexity in the partnership behind Tingbjerg Changing Diabetes (TCD), a community-based intervention addressing health and social development in the disadvantaged neighborhood of Tingbjerg in urban Copenhagen. The study provides important insights on the role of context and how it contributes complexity in community-based health promotion.The study is based on 18 months of ethnographic fieldwork in the local community including participant observations and 9 in-depth interviews with key partner representatives. Findings show that complexity in TCD can be characterized by unpredictability in actions and outcomes, undefined purpose and direction, and differing organizational logics. Factors that support partners' navigation in complexity include connectivity, embracing a flexible intervention framework, autonomy, and quick responsiveness. The study showcases the interdependency between the intervention and the context of the disadvantaged neighborhood of Tingbjerg and encourages stakeholders and researchers to embrace the messiness of complexity, and to pay attention to ways through which messiness and unpredictability can be handled.

All Models Are Wrong, and Some Are Religious: Supernatural Explanations as Abstract and Useful Falsehoods about Complex Realities

Many cognitive and evolutionary theories of religion argue that supernatural explanations are byproducts of our cognitive adaptations. An influential argument states that our supernatural explanations result from a tendency to generate anthropomorphic explanations, and that this tendency is a byproduct of an error management strategy because agents tend to be associated with especially high fitness costs. We propose instead that anthropomorphic and other supernatural explanations result as features of a broader toolkit of well-designed cognitive adaptations, which are designed for explaining the abstract and causal structure of complex, unobservable, and uncertain phenomena that have substantial impacts on fitness. Specifically, we argue that (1) mental representations about the abstract vs. the supernatural are largely overlapping, if not identical, and (2) when the data-generating processes for scarce and ambiguous observations are complex and opaque, a naive observer can improve a bias-variance trade-off by starting with a simple, underspecified explanation that Western observers readily interpret as "supernatural." We then argue that (3) in many cases, knowledge specialists across cultures offer pragmatic services that involve apparently supernatural explanations, and their clients are frequently willing to pay them in a market for useful and effective services. We propose that at least some ethnographic descriptions of religion might actually reflect ordinary and adaptive responses to novel problems such as illnesses and natural disasters, where knowledge specialists possess and apply the best available explanations about phenomena that would otherwise be completely mysterious and unpredictable.

Historical and projected changes in hydrological and sediment connectivity under climate change in a tropical catchment of Mexico

Hydrological and erosion dynamics are prone to change due to natural factors, human activities, or climate change. These changes are mainly related to modifications of land use and cover and can be assessed through the concept of connectivity, which analyzes how the spatial distribution of the elements facilitates runoff and sediment transport. The objective of this study was to evaluate changes in hydrological and sediment connectivity over 42 years and projected under a climate change scenario in the tropical Santa Cruz catchment in Aquismón, S.L.P., Mexico. The index of connectivity (IC) was computed using SedInConnect version 2.3 and the ArcSWAT model to estimate runoff. Hydrological connectivity and runoff were projected for 2027 using the MPI ECHAM 5 in the A2 climate change scenario. The results indicated that spatio-temporal changes in land use/cover, in conjunction with geomorphological features and expected climate change, would modify hydrological and sediment connectivity, especially in flat areas, where conversion of natural vegetation to cropland was steadily increasing over the years. Under future conditions, runoff and sediment transport are likely to increase, which will impact soil erosion and vulnerability to flooding but will not necessarily be negative. The study shows how spatial-temporal integration of runoff, sediments, landforms, land use cover and change, and connectivity can improve our understanding of catchment dynamics and the importance of analyses that characterize their evolution. The results can subsequently be applied and replicated in other catchments for management and restoration purposes.

Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro

Cascading activity is commonly observed in complex dynamical systems, including networks of biological neurons, and how these cascades spread through the system is reliant on how the elements of the system are connected and organized. In this work, we studied networks of neurons as they matured over 50 days in vitro and evaluated both their dynamics and their functional connectivity structures by observing their electrophysiological activity using microelectrode array recordings. Correlations were obtained between features of their activity propagation and functional connectivity characteristics to elucidate the interplay between dynamics and structure. The results indicate that in vitro networks maintain a slightly subcritical state by striking a balance between integration and segregation. Our work demonstrates the complementarity of these two approaches—functional connectivity and avalanche dynamics—in studying information propagation in neurons in vitro, which can in turn inform the design and optimization of engineered computational substrates.

Cancer and Chaos and the Complex Network Model of a Multicellular Organism

Simple Summary

Currently, knowledge on chaos has developed rapidly, and the link between cancer and “genomic chaos” seems obvious. Hopes for a deeper understanding of cancer, allowing cancer modeling, therefore relate to the meaning of the term “chaos”. It has many meanings, however. Chaos theory and medicine are conceptually quite distant, requiring the comparison and agreement of terms. This article was written by three authors whose fields cover both medical problems and complex dynamic networks suitable for modeling cancer, including chaotic phenomena. The article provides, first of all, a coherent, common interpretative basis linking chaos with modeling tools, which should significantly facilitate teams of specialists from various fields to undertake specific work on simulating cancer-related phenomena.

Abstract

In the search of theoretical models describing cancer, one of promising directions is chaos. It is connected to ideas of “genome chaos” and “life on the edge of chaos”, but they profoundly differ in the meaning of the term “chaos”. To build any coherent models, notions used by both ideas should be firstly brought closer. The hypothesis “life on the edge of chaos” using deterministic chaos has been radically deepened developed in recent years by the discovery of half-chaos. This new view requires a deeper interpretation within the range of the cell and the organism. It has impacts on understanding “chaos” in the term “genome chaos”. This study intends to present such an interpretation on the basis of which such searches will be easier and closer to intuition. We interpret genome chaos as deterministic chaos in a large module of half-chaotic network modeling the cell. We observed such chaotic modules in simulations of evolution controlled by weaker variant of natural selection. We also discuss differences between free and somatic cells in modeling their disturbance using half-chaotic networks.

From Isles of Königsberg to Islets of Langerhans: Examining the Function of the Endocrine Pancreas Through Network Science

Islets of Langerhans are multicellular microorgans located in the pancreas that play a central role in whole-body energy homeostasis. Through secretion of insulin and other hormones they regulate postprandial storage and interprandial usage of energy-rich nutrients. In these clusters of hormone-secreting endocrine cells, intricate cell-cell communication is essential for proper function. Electrical coupling between the insulin-secreting beta cells through gap junctions composed of connexin36 is particularly important, as it provides the required, most important, basis for coordinated responses of the beta cell population. The increasing evidence that gap-junctional communication and its modulation are vital to well-regulated secretion of insulin has stimulated immense interest in how subpopulations of heterogeneous beta cells are functionally arranged throughout the islets and how they mediate intercellular signals. In the last decade, several novel techniques have been proposed to assess cooperation between cells in islets, including the prosperous combination of multicellular imaging and network science. In the present contribution, we review recent advances related to the application of complex network approaches to uncover the functional connectivity patterns among cells within the islets. We first provide an accessible introduction to the basic principles of network theory, enumerating the measures characterizing the intercellular interactions and quantifying the functional integration and segregation of a multicellular system. Then we describe methodological approaches to construct functional beta cell networks, point out possible pitfalls, and specify the functional implications of beta cell network examinations. We continue by highlighting the recent findings obtained through advanced multicellular imaging techniques supported by network-based analyses, giving special emphasis to the current developments in both mouse and human islets, as well as outlining challenges offered by the multilayer network formalism in exploring the collective activity of islet cell populations. Finally, we emphasize that the combination of these imaging techniques and network-based analyses does not only represent an innovative concept that can be used to describe and interpret the physiology of islets, but also provides fertile ground for delineating normal from pathological function and for quantifying the changes in islet communication networks associated with the development of diabetes mellitus.

The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience

This paper highlights the relevance of connectivity and its architecture as a general conceptual framework which underlies and integrates the concepts of network vulnerability, complexity, and resilience. In particular, it will be pointed out that connectivity architecture can be considered an explicit key element for network vulnerability and shock propagation. While the relevance of the various connectivity configurations is not clearly emphasised in the dynamic complexity models of the space-economy, it appears to play a primary role in network analysis. In this regard, the emerging recognition of connectivity architecture in relation to hubs ‒ and hierarchies of hubs ‒ in a complex network will help the enhancement of network resilience. The paper develops as follows. First, the notion of network vulnerability, which refers not only to the phenomenon of shocks, but also to the propagation of shocks in a network, will be examined. Here it appears that modelling vulnerability and shock propagation, also jointly with cascading disaster models, is strongly based on connectivity issues. The question is: How can conventional (complex) system dynamic modelling, as well as network modelling, take into account these shocks and connectivity dynamics from the methodological viewpoint? A review in this respect shows how connectivity is a 'hidden' element in these complexity models, for example, in chaos or (dynamic) competition models, where interaction parameter values might lead to vulnerable domains and chaotic behaviour. On the contrary, connectivity and its various topologies have a distinct, primary role in network analysis. The issue of network resilience appears therefore to be the 'response' to vulnerability and chaos, calling for robustness and stability of the network in the presence of shocks and disruptions. Resilience analysis refers to the speed at which a network returns to its equilibrium after a shock, as well as to the perturbations/shocks that can be absorbed before the network is induced into some other equilibrium (adaptivity). Connectivity is relevant here, but not often considered in spatial economics. In order to reach a unified methodological framework, attention will finally be paid to a complementary analysis of the (dynamic) concepts of vulnerability and resilience. In this light, chaos models/properties might be seen in a positive perspective, since small changes can lead to uncertain and unstable effects, but also, thanks to connectivity, to new equilibria which are not necessarily negative. Thus, the architecture of connectivity, in its interdisciplinary insights, can be considered as a fundamental (and analytical) approach for identifying vulnerability and resilience patterns in complex networks.

A rapid and inexpensive colour-based sediment tracing method incorporating hydrogen peroxide sample treatment as an alternative to quantitative source fingerprinting for catchment management

Accessible sediment provenance information is highly desirable for guiding targeted interventions for reducing excess diffuse agricultural sediment losses to water. Conventional sediment source fingerprinting methods can provide this information, but at high cost, thereby limiting their widespread application for catchment management. The use of sediment colour measured using an office document scanner represents an easy, fast, and inexpensive alternative method to trace sediment sources. However, the potential for poor source discrimination and its non-conservatism due to enrichment in sediment organic matter content during sediment transport represent possible limitations to its use. As such, the treatment of samples using hydrogen peroxide to remove organic matter can potentially improve source discrimination based upon geology or soil type, and the mapping of differences in colour between source and sediment samples removing the need for a priori source groups, were trialled in a new colour-based tracing framework. The River Avon in southwest England and Holbeck/Wath Beck in northeast England were studied as they have been identified as being of high priority for the targeting of on-farm advice delivered through a long-running agri-environment initiative. In both catchments, colour was effective at identifying that a small proportion of each which would be considered as being low erosion risk was the dominant source of the sampled sediment. This was due to poor connectivity between fields deemed to be at high risk of erosion and stream channels. The hydrogen peroxide sample treatment confirmed that sediment colour was not significantly altered by enrichment in organic matter content. This treament and the mapped comparison between source and suspended sediment colour improved source discrimination allowing for the more spatially-refined identification of critical sediment source areas. It is argued that this new inexpensive procedure can potentially deliver more precise and reliable information to catchment managers than costly quantitative sediment source fingerprinting methods. This method can greatly increase the availability of catchment-specific sediment source data and therefore the robust targeting of management efforts on a national scale.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.

Semi-Adaptive Evolution with Spontaneous Modularity of Half-Chaotic Randomly Growing Autonomous and Open Networks

Up until now, studies of Kauffman network stability have focused on the conditions resulting from the structure of the network. Negative feedbacks have been modeled as ice (nodes that do not change their state) in an ordered phase but this blocks the possibility of breaking out of the range of correct operation. This first, very simplified approximation leads to some incorrect conclusions, e.g., that life is on the edge of chaos. We develop a second approximation, which discovers half-chaos and shows its properties. In previous works, half-chaos has been confirmed in autonomous networks, but only using node function disturbance, which does not change the network structure. Now we examine half-chaos during network growth by adding and removing nodes as a disturbance in autonomous and open networks. In such evolutions controlled by a ‘small change’ of functioning after disturbance, the half-chaos is kept but spontaneous modularity emerges and blurs the picture. Half-chaos is a state to be expected in most of the real systems studied, therefore the determinants of the variability that maintains the half-chaos are particularly important in the application of complex network knowledge.

The coupled socio-ecohydrological evolution of river systems: Towards an integrative perspective of river systems in the 21st century

River systems have undergone a massive transformation since the Anthropocene. The natural properties of river systems have been drastically altered and reshaped, limiting the use of management frameworks, their scientific knowledge base and their ability to provide adequate solutions for current problems and those of the future, such as climate change, biodiversity crisis and increased demands for water resources. To address these challenges, a socioecologically driven research agenda for river systems that complements current approaches is needed and proposed. The implementation of the concepts of social metabolism and the colonisation of natural systems into existing concepts can provide a new basis to analyse the coevolutionary coupling of social systems with ecological and hydrological (i.e., 'socio-ecohydrological') systems within rivers. To operationalize this research agenda, we highlight four initial core topics defined as research clusters (RCs) to address specific system properties in an integrative manner. The colonisation of natural systems by social systems is seen as a significant driver of the transformation processes in river systems. These transformation processes are influenced by connectivity (RC 1), which primarily addresses biophysical aspects and governance (RC 2), which focuses on the changes in social systems. The metabolism (RC 3) and vulnerability (RC 4) of the social and natural systems are significant aspects of the coupling of social systems and ecohydrological systems with investments, energy, resources, services and associated risks and impacts. This socio-ecohydrological research agenda complements other recent approaches, such as 'socio-ecological', 'socio-hydrological' or 'socio-geomorphological' systems, by focusing on the coupling of social systems with natural systems in rivers and thus, by viewing the socioeconomic features of river systems as being just as important as their natural characteristics. The proposed research agenda builds on interdisciplinarity and transdisciplinarity and requires the implementation of such programmes into the education of a new generation of river system scientists, managers and engineers who are aware of the transformation processes and the coupling between systems.

Nanotechnology Approaches for Enhanced CNS Drug Delivery in the Management of Schizophrenia

Schizophrenia is a neuropsychiatric disorder mainly affecting the central nervous system, presented with auditory and visual hallucinations, delusion and withdrawal from society. Abnormal dopamine levels mainly characterise the disease; various theories of neurotransmitters explain the pathophysiology of the disease. The current therapeutic approach deals with the systemic administration of drugs other than the enteral route, altering the neurotransmitter levels within the brain and providing symptomatic relief. Fluid biomarkers help in the early detection of the disease, which would improve the therapeutic efficacy. However, the major challenge faced in CNS drug delivery is the blood-brain barrier. Nanotherapeutic approaches may overcome these limitations, which will improve safety, efficacy, and targeted drug delivery. This review article addresses the main challenges faced in CNS drug delivery and the significance of current therapeutic strategies and nanotherapeutic approaches for a better understanding and enhanced drug delivery to the brain, which improve the quality of life of schizophrenia patients.

Two classes of functional connectivity in dynamical processes in networks

The relationship between network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of recent years. Understanding this relationship is of relevance to a range of disciplines-from neuroscience to geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity, SC) with a (network) representation of the dynamics (functional connectivity, FC). Here, we show that one can distinguish two classes of functional connectivity-one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes-excitations, regular and chaotic oscillators-and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in geomorphology, ecology, systems biology, neuroscience and socio-ecological systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How

Many diseases that affect modern humans fall in the category of complex diseases, thus called because they result from a combination of multiple aetiological and pathogenic factors. Regardless of the organ or system affected, complex diseases present major challenges in diagnosis, classification, and management. Current forms of therapy are usually applied in an indiscriminate fashion based on clinical information, but even the most advanced drugs only benefit a limited number of patients and to a variable and unpredictable degree. This 'one measure does not fit all' situation has spurred the notion that therapy for complex disease should be tailored to individual patients or groups of patients, giving rise to the notion of 'precision medicine' [PM]. Inflammatory bowel disease [IBD] is a prototypical complex disease where the need for PM has become increasingly clear. This prompted the European Crohn's and Colitis Organisation to focus the Seventh Scientific Workshop on this emerging theme. The articles in this special issue of the Journal address the various complementary aspects of PM in IBD, including what PM is; why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM in IBD. This first article of this series is structured on three simple concepts [what, why, and how] and addresses the definition of PM, discusses the rationale for the need of PM in IBD, and outlines the methodology required to implement PM in IBD in a correct and clinically meaningful way.

Characterization of real-world networks through quantum potentials

Network connectivity has been thoroughly investigated in several domains, including physics, neuroscience, and social sciences. This work tackles the possibility of characterizing the topological properties of real-world networks from a quantum-inspired perspective. Starting from the normalized Laplacian of a network, we use a well-defined procedure, based on the dressing transformations, to derive a 1-dimensional Schrödinger-like equation characterized by the same eigenvalues. We investigate the shape and properties of the potential appearing in this equation in simulated small-world and scale-free network ensembles, using measures of fractality. Besides, we employ the proposed framework to compare real-world networks with the Erdős-Rényi, Watts-Strogatz and Barabási-Albert benchmark models. Reconstructed potentials allow to assess to which extent real-world networks approach these models, providing further insight on their formation mechanisms and connectivity properties.

Delivering the Promise of Sendai Framework for Disaster Risk Reduction in Fragile and Conflict-Affected Contexts (FCAC): A Case Study of the NGO GOAL's Response to the Syria Conflict

The Sendai Framework for Disaster Risk Reduction (SFDRR) has helped to reduce global disaster risk, but there has been a lack of progress in disaster risk reduction (DRR) for people living in fragile and conflict affected contexts (FCAC). Given the mounting evidence that DRR cannot be implemented through conventional approaches in FCAC, serious efforts must be made to understand how to meet SFDRR's goals. This paper offers a case study of international non-governmental organization GOAL's programming that responds to the protracted crisis in Syria, with critical discussion on SFDRR and how to adapt humanitarian relief and disaster resilience.

Vegetation and Geomorphic Connectivity in Mountain Fluvial Systems

Rivers are complex biophysical systems, constantly adjusting to a suite of changing governing conditions, including vegetation cover within their basins. This review seeks to: (i) highlight the crucial role that vegetation’s influence on the efficiency of clastic material fluxes (geomorphic connectivity) plays in defining mountain fluvial landscape’s behavior; and (ii) identify key challenges which hinder progress in the understanding of this subject. To this end, a selective literature review is carried out to illustrate the pervasiveness of the plants’ effects on geomorphic fluxes within channel networks (longitudinal connectivity), as well as between channels and the broader landscape (lateral connectivity). Taken together, the reviewed evidence lends support to the thesis that vegetation-connectivity linkages play a central role in regulating geomorphic behavior of mountain fluvial systems. The manuscript is concluded by a brief discussion of the need for the integration of mechanistic research into the local feedbacks between plants and sediment fluxes with basin-scale research that considers emergent phenomena.

Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

Creative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

Managing sediment (dis)connectivity in fluvial systems

Globally, rivers systems are under considerable and increasing threat from multiple anthropogenic stresses, including different types of direct (e.g. channel engineering) and indirect human impacts (e.g. land cover and land use changes) that alter water and sediment dynamics. (Dis)connectivity relationships determine the source, timing and rates of water and sediment flux in catchments and thus their geomorphic sensitivity to disturbance. However, most river and catchment management plans overlook the role of sediment (dis)connectivity. Here we use examples from different environmental settings with different sediment-related problems to show how understandings of sediment (dis)connectivity can inform catchment-based management plans. We focus on concerns for river conservation and recovery, using examples from Austria, New Zealand and Australia. Finally, we present questions for practitioners to consider to appropriately contextualise management applications when using (dis)connectivity concepts in practice. Our findings revealed that differences in sediment (dis)connectivity relationships exert profound catchment-specific variability in (eco)-geomorphic response to disturbance. Understanding (dis)connectivity and system history is therefore essential to forecast the effects of on-ground management actions.

Connectivity and hydrological efficiency dynamics at active volcanoes, Mexico

Connectivity is an emergent property that describes how complex topography favors or impedes sediment transfer processes. In active volcanic areas, high connectivity may lead to extremely efficient processes, such as lahars. The aim of the present study is to examine the behavior (activation-deactivation) of sub-basins affected by volcanic and anthropogenic processes by studying the changes in connectivity and hydrological efficiency. Two volcanic zones in Mexico were selected: Volcán de Colima and Popocatépetl volcano, the two most active and dangerous volcanoes in the country. The joint index of connectivity (IC_J) and lateral hydrological efficiency index (LHEI) were calculated for both volcanic areas in basins recently affected by eruptive activity (Volcán de Colima) and co-seismic landslides (Popocatépetl). The analyses enabled the identification of eleven recently activated sub-basins (3.82 km²) at the Volcán de Colima and fifteen (3.77 km²) at the Popocatepetl volcano, as a consequence of natural processes and economic activities. Critical thresholds indicating the percentage area of land cover/use at which a sub-basin reaches high or very high LHEI values and the percentage of land cover/use change required for a sub-basin to modify its behavior (activation-deactivation) were identified using classification trees. The holistic capacity of the concepts of connectivity and hydrological efficiency permits analyzing the spatiotemporal variations of sediment transport based on the interactions between the hydrogeomorphological dynamics of volcanic processes and the territorial impact of socio-economical activities. Through this approach, new active areas have been identified in both volcanoes; the knowledge of the processes that occurred in these areas represents a key factor for hazard and risk assessment for the population in the near future.

Channel and cut-bluff failure connectivity in a river system: Case study of the braided-wandering Belá River, Western Carpathians, Slovakia

Straightforward sediment transport is not common in nature and material is storage during transport and reworked by the same processed that lead to initial mass erosion. Despite the development of quantitative assessment by application high accuracy topography measurement, lack of conceptualisation and combination with precise elevation model changes is still missing. This paper presents a field-based channel-bluff connectivity study based on a sediment cascade approach. A TLS (terrestrial laser scanning) time-series database was generated by systematic monitoring of cut-bluff slope surface of the braided-wandering Belá River. The database was used to estimate volume changes and allowed to develop the conceptualisation model of coupling of cut-bluff slope based on spatial and temporal analyses of channel hydrology, gravity conditioned transformation of matter and a detailed of sediment budget calculations. Historical analyses have shown that a flow direction perpendicular to the slope is crucial to activate cut-bluff slope material movement and initiate a sediment cascade, as significant contributors of sediment into the river. Sediment supply to the channels correlates with the magnitude of flood events (maximum discharge, cumulative discharge, cumulative discharge higher than RI_1.5, and duration of discharges higher than RI_1.5) and lateral migration as a main factor controlling the behaviour of the cut-bluff slope-channel system. During the survey from March 2016 to November 2018, were transported 10,103 m³ (25,964 t) of fine-grained sediment into the river channel.

Social-ecological network analysis for sustainability sciences: a systematic review and innovative research agenda for the future

Social-ecological network (SEN) concepts and tools are increasingly used in human-environment and sustainability sciences. We take stock of this budding research area to further show the strength of SEN analysis for complex human-environment settings, identify future synergies between SEN and wider human-environment research, and provide guidance about when to use different kinds of SEN approaches and models. We characterize SEN research along a spectrum specifying the degree of explicit network representation of system components and dynamics. We then systematically review one end of this spectrum, what we term "fully articulated SEN" studies, which specifically model unique social and ecological units and relationships. Results show more focus on methodological advancement and applied ends. While there has been some development and testing of theories, this remains an area for future work and would help develop SENs as a unique field of research, not just a method. Authors have studied diverse systems, while mainly focused on the problem of social-ecological fit alongside a scattering of other topics. There is strong potential, however, to engage other issues central to human-environment studies. Analyzing the simultaneous effects of multiple social, environmental, and coupled processes, change over time, and linking network structures to outcomes are also areas for future advancement. This review provides a comprehensive assessment of (fully articulated) SEN research, a necessary step that can help scholars develop comparable cases and fill research gaps.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Social-ecological alignment and ecological conditions in coral reefs

Complex social-ecological interactions underpin many environmental problems. To help capture this complexity, we advance an interdisciplinary network modeling framework to identify important relationships between people and nature that can influence environmental conditions. Drawing on comprehensive social and ecological data from five coral reef fishing communities in Kenya; including interviews with 648 fishers, underwater visual census data of reef ecosystem condition, and time-series landings data; we show that positive ecological conditions are associated with ‘social-ecological network closure’ – i.e., fully linked and thus closed network structures between social actors and ecological resources. Our results suggest that when fishers facing common dilemmas form cooperative communication ties with direct resource competitors, they may achieve positive gains in reef fish biomass and functional richness. Our work provides key empirical insight to a growing body of research on social-ecological alignment, and helps to advance an integrative framework that can be applied empirically in different social-ecological contexts.

The relationships between people can have important consequences for the systems they depend on. Here the authors show that when coral reef fishers face commons dilemmas, the formation of cooperative communication with competitors can lead to positive gains in reef fish biomass and functional richness.

Soil erosion on the "El Portalet" mountain trails in the Eastern Iberian Peninsula

Leisure activities in natural areas have experienced a large increase in popularity. In Mediterranean ecosystems, research on soil erosion in agriculture and forest fire affected land has been well developed but trail erosion has not been widely studied. In this work we evaluated two trails in the eastern Iberian Peninsula to assess soil erosion rates and provide new data. The study area is located in a semi-arid Mediterranean climate (around 400 mm y^-1) and in limestone terrain with scarce soil development. The average erosion rates were 24.39 Mg ha^-1 y^-1 and 40.40 Mg ha^-1 y^-1 for the "Barranc de la Cova de la Hedra" and "Casa del Racó" study trails, respectively. These are non-sustainable rates due to the shallow soils and slow soil development in Mediterranean ecosystems. We found that the depth at the centre of the trail and the maximum depth measured could quickly and easily assess soil erosion rates in the mountain trails. Measuring the width and depth in the centre of the trail can assess 91% of the total erosion and reduces the time of the survey by 80%.

Assessing flood probability for transportation infrastructure based on catchment characteristics, sediment connectivity and remotely sensed soil moisture

Flooding may damage important transportation infrastructures, such as roads, railways and bridges, which need to be well planned and designed to be able to withstand current and possible future climate-driven increases in flood frequencies and magnitudes. This study develops a novel approach to predictive statistical modelling of the probability of flooding at major road-stream intersection sites, where water, sediment and debris can accumulate and cause failure of drainage facilities and associated road damages. Two areas in south-west Sweden, affected by severe floods in August 2014, are used in representative case studies for this development. A set of physical catchment-descriptors (PCDs), characterizing key aspects of topography, morphology, soil type, land use, hydrology (precipitation and soil moisture) and sediment connectivity in the water- and sediment-contributing catchments, are used for the predictive flood modelling. A main novel contribution to such modelling is to integrate the spatiotemporal characteristics of remotely-sensed soil moisture in indices of sediment connectivity (IC), thereby also allowing for investigation of the role of soil moisture in the flood probability for different road-stream intersections. The results suggest five categories of PCDs as especially important for flood probability quantification and identification of particularly flood-prone intersections along roads (railways, etc.) These include: channel slope at the road-stream intersection and average elevation, soil properties (mainly percentage of till), land use cover (mainly percentage of urban areas), and a sediment connectivity index that considers soil moisture in addition to morphology over the catchment.

Rainfall-simulated quantification of initial soil erosion processes in sloping and poorly maintained terraced vineyards - Key issues for sustainable management systems

In the context of the Sustainable Development Goals (SDG), understanding landscape evolution is essential to design long-term management plans. In agricultural fields, such as the vineyards on steep slopes, the terraces offer one of the most important morphological changes. However, it is not clear if the poorly managed agricultural terraces are optimal to reduce soil erosion and overland flow, although the trafficability is improved. Therefore, the main aim of this research is to compare the differences between initial soil erosion processes on poorly managed terraced vineyards and sloping vineyards at the pedon scale, considering the key role of the SSC (Soil Surface Components). To achieve this goal, twenty-six rainfall simulations were performed, considering the inclination, vegetation and stone covers, and surface roughness. Our research was carried out in the sloping vineyards (>20°) of the Almáchar municipality, in the Montes de Málaga (Spain). Those vineyards are characterized by bare soils, low organic matter and high rock fragment contents. Our results showed that higher soil losses (42.2 g m^-2vs 9.4 g m^-2) and runoff (4.9 l m^-2vs 1.6 l m^-2) were detected in the plots of the poorly managed terraced vineyard than in the sloping one. Moreover, the time to runoff generation was lower in the poorly conserved terraces (232 s) than in the sloping vineyard (679 s), showing a faster saturation capacity. The SSC considered as the key factors were the reduction of the stone cover and an increase of roughness. As a conclusion, we confirm that the imminent transformation from sloping vineyards into terraced fields could lead several land degradation processes if a poor management is carried out, and no control measures are applied during the process, such as the conservation of stone walls or vegetation cover above the embankment, which is not in compliance with the SDG.

Changes in connectivity and hydrological efficiency following wildland fires in Sierra Madre Oriental, Mexico

Fire modifies soil surface, and hence soil hydrological properties change after wildland fires. High fire severity causes partial or total removal of vegetation, reduction of soil aggregate stability and increased water repellency, which are associated with high runoff and erosion. The spatial connection among these runoff sources is an important factor to consider when evaluating fire-induced changes on hillslope and catchment hydrology, as fire generates connected areas of bare soil, which may increase hydrological connectivity and hence post-fire runoff and erosion. The aim of this study was to quantify changes in hydrological connectivity and efficiency in two burned areas in central Mexico. By integrating rainfall simulation and spatial analysis, an index of connectivity (IC) and the lateral hydrological efficiency index (LHEI) were computed based on land/cover use, fire severity and topography within 287 burned sub-basins. Post-fire IC and LHEI were compared with the pre-fire scenario, and the relationship between LHEI and the proportion of burned area was assessed at the sub-basin level. Thresholds of the burned area per fire severity needed to increase LHEI were determined by a classification tree. The index of connectivity and LHEI were higher after wildland fires. The burned area was positively related with LHEI, and at least 43.3% of area burned with high severity is sufficient to produce the highest LHEI. The results are evidence of the effect of fire on hydrological connectivity and efficiency which adds to the understanding of fire-hydrology relations and can be used for integrated catchment management, ecological restoration and risk assessment.

Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism

Relational interlinked dependencies between concepts constitute the structure of abstract knowledge and are crucial in learning conceptual knowledge and the meaning of concepts. To explore pre-service teachers’ declarative knowledge of physics concepts, we have analyzed concept networks, which agglomerate 12 pre-service teacher students’ representations of the key elements in electricity and magnetism. We show that by using network-based methods, the interlinked connections of nodes, locally and globally, can be analyzed to reveal how different elements of the network are supported through their connections to other nodes in the network. Nodes with high global connectivity initialize contiguous concept patchworks within the network and are thus most often found to be abstract, general, and advanced concepts. Locally cohesive concepts, on the other hand, are nearly always auxiliary supporting concepts, related to specific textbook-type experiments and model-type conceptional elements. Comparisons of group-level knowledge and individual pre-service teacher students’ knowledge in the form of networks shows that while in group-level the aggregated knowledge is expert-like, at the individual level pre-service teacher students possess only a fraction of that knowledge.

Identifying the Key Information and Land Management Plans for Water Conservation under Dry Weather Conditions in the Border Areas of the Syr Darya River in Kazakhstan

Due to an increase in poorly planned anthropogenic activities, the water quality of several Asian big rivers is highly being affected. Although the assessment of heavy metal contents is vital to develop and design sustainable water management plans, several areas in Central Asia such as Kazakhstan do not have recent studies available that evaluate this situation. One representative example of this lack of information is the Syr Darya River. Thus, this study carried out the first approach to a water quality assessment in Kazakhstan’s Syr Darya River, where a massive expansion of irrigation canals, pastures in middle- and lower-reaches and an increase in industrialization and population have lowered its potential water capacity. To achieve this goal, various physicochemical parameters were analyzed for forty-three water samples along the river under dry weather conditions at 25 cm water depth. The obtained results were analyzed using standard methods (e.g., Multi N/C 2100 S analyzer or an atomic absorption spectrometer) and evaluated by multivariate techniques (cluster analysis (CA), principal component analysis (PCA), and non-metric multidimensional scaling (NDMS)) and a heavy metal pollution index (HPI). In the CA, five cluster groups were obtained. It is important to remark that the first cluster consists of the highest number of water sampling points (8). The last cluster is made up of only one point, which shows the highest difference against the other sites in our model. The NDMS also confirmed that some specific points along the river are different. Five components were extracted from the PCA: (1) COD (chemical oxygen demand), Zn, Cu, Pb, Ni and Mn; (2) Cu, Cd, Ni and Co; (3) T (water temperature), pH and DO (dissolved oxygen); (4) T and Fe; and (5) COD and OC (organic carbon). The HPI showed very high values (279.9), which were locally confirmed in some hotspots close to the Aral Sea, industrialized areas and agricultural fields. Therefore, our results demonstrate that, under dry weather conditions, surface water resources could be mismanaged in the Syr Darya River in Kazakhstan in specific areas. For the future, considering the important role that agriculture and pasture play in the Kazakh economy, we insist upon the importance of applying water quality control measures applying nature-based solutions and efficient management plans. Moreover, we confirmed the necessity to conduct further research related to sampling under other weather situations such as wet and cold conditions, different river water depths and other locations considering specific land uses, for example, grazing, mining, railways or industries.