Phytoplankton communities are crucial components of aquatic ecosystems, and since they are highly interactive, they always form complex networks. Yet, our understanding of how interactive phytoplankton networks vary through time under changing environmental conditions is limited. Using a 29-year (339 months) long-term dataset on Lake Taihu, China, we constructed a temporal network comprising monthly sub-networks using \"extended Local Similarity Analysis\" and assessed how eutrophication, climate change, and restoration efforts influenced the temporal dynamics of network complexity and stability. The network architecture of phytoplankton showed strong dynamic changes with varying environments. Our results revealed cascading effects of eutrophication and climate change on phytoplankton network stability via changes in network complexity. The network stability of phytoplankton increased with average degree, modularity, and nestedness and decreased with connectance. Eutrophication (increasing nitrogen) stabilized the phytoplankton network, mainly by increasing its average degree, while climate change, i.e., warming and decreasing wind speed enhanced its stability by increasing the cohesion of phytoplankton communities directly and by decreasing the connectance of network indirectly. A remarkable shift and a major decrease in the temporal dynamics of phytoplankton network complexity (average degree, nestedness) and stability (robustness, persistence) were detected after 2007 when numerous eutrophication mitigation efforts (not all successful) were implemented, leading to simplified phytoplankton networks and reduced stability. Our findings provide new insights into the organization of phytoplankton networks under eutrophication (or re-oligotrophication) and climate change in subtropical shallow lakes.

BACKGROUND: Approaches to prevent and manage diabetes at a community population level are hindered because current strategies are not aligned with the structure and function of a community system. We describe a community-driven process based on local data and rapid prototyping as an alternative approach to create diabetes prevention and care management solutions appropriate for each community. We report on the process and provide baseline data for a 3-year case study initiative to improve diabetes outcomes in two rural Nebraska communities.
METHODS: We developed an iterative design process based on the assumption that decentralized decision-making using local data feedback and monitoring will lead to the innovation of local sustainable solutions. Coalitions act as community innovation hubs and meet monthly to work through a facilitated design process. Six core diabetes measures will be tracked over the course of the project using the electronic health record from community clinics as a proxy for the entire community.
RESULTS: Baseline data indicate two-thirds of the population in both communities are at risk for prediabetes based on age and body mass index. However, only a fraction (35% and 12%) of those at risk have been screened. This information led both coalitions to focus on improving screening rates in their communities.
CONCLUSIONS: In order to move a complex system towards an optimal state (e.g., improved diabetes outcomes), stakeholders must have access to continuous feedback of accurate, pertinent information in order to make informed decisions. Conventional approaches of implementing evidence-based interventions do not facilitate this process.

UNASSIGNED: Under the backdrop of pervasive health inequalities, public health professionals, researchers and non-academic partners in the United Kingdom are mobilising to understand how and in what ways community assets can address health disparities at scale in complex systems. While there is recognition that cultural, natural and community resources can improve health outcomes, these are unequally dispersed with lack of integration in communities and health and social care systems. Researching Evidence-based Alternatives in Living, Imaginative, Traumatised, Integrated, Embodied Systems (REALITIES) is a participatory action research Scottish consortium of 57 with established community asset hubs in five localities with strong relationships uniting conflicting ways of seeing the world. Our collective of lived and felt experience community members, community-embedded researchers, academics and non-academics draws upon a variety of practices, methods, datasets and philosophies to expand existing approaches to tackling health inequalities.
UNASSIGNED: We present conceptual and theoretical underpinnings for our co-produced systems-level model and empirical findings from testing REALITIES across three disadvantaged localities (November 2022, ongoing). After explaining the context that led to the development of the new scalable REALITIES model for integrated public systems to interface with \'assets\', we detail philosophical pillars and guiding principles for our model and how we applied these mechanisms to explain how integrated partnership working can lead to improved health outcomes across multiple public systems.
UNASSIGNED: We present a meta-analysis from co-producing and testing the model, showing how measuring change in complex public systems involves critical investigation of People, Process, Place, Price, Power and Purpose. Our critique reflects on power imbalances and inequities in Research-practice-Policy (RPP) partnerships and suggestions for how to nurture healthy ecosystems: overcoming barriers and enabling participation; reflecting on challenges of scaling up, testability and complexity of RPP partnerships; moving from siloed learning to transdisciplinary collaboration in practice; ensuring knowledge exchange has direct impact on communities and frontline practitioners; embedding relational ethics and safeguarding into daily practice.
UNASSIGNED: We propose the REALITIES model to unite alternative, sometimes conflicting, ways of thinking about public systems and community assets by continuously reflecting on entanglements between different assumptions about knowledge, reality, evidence, and unnecessary binaries between creative methodologies and scientific method.

BACKGROUND: As healthcare systems rapidly become more complex, healthcare leaders are navigating expanding role scopes and increasingly varied tasks to ensure the provision of high-quality patient care. Despite a range of leadership theories, models, and training curricula to guide leadership development, the roles and competencies required by leaders in the context of emerging healthcare challenges (e.g., disruptive technologies, ageing populations, and burnt-out workforces) have not been sufficiently well conceptualized. This scoping review aimed to examine these roles and competencies through a deep dive into the contemporary academic and targeted gray literature on future trends in healthcare leadership roles and competencies.
METHODS: Three electronic databases (Business Source Premier, Medline, and Embase) were searched from January 2018 to February 2023 for peer-reviewed literature on key future trends in leadership roles and competencies. Websites of reputable healthcare- and leadership-focused organizations were also searched. Data were analyzed using descriptive statistics and thematic analysis to explore both the range and depth of literature and the key concepts underlying leadership roles and competencies.
RESULTS: From an initial 348 articles identified in the literature and screened for relevance, 39 articles were included in data synthesis. Future leadership roles and competencies were related to four key themes: innovation and adaptation (e.g., flexibility and vision setting), collaboration and communication (e.g., relationship and trust building), self-development and self-awareness (e.g., experiential learning and self-examination), and consumer and community focus (e.g., public health messaging). In each of these areas, a broad range of strategies and approaches contributed to effective leadership under conditions of growing complexity, and a diverse array of contexts and situations for which these roles and competencies are applicable.
CONCLUSIONS: This research highlights the inherent interdependence of leadership requirements and health system complexity. Rather than as sets of roles and competencies, effective healthcare leadership might be better conceptualized as a set of broad goals to pursue that include fostering collaboration amongst stakeholders, building cultures of capacity, and continuously innovating for improved quality of care.

The paper highlights the necessity for a robust regulatory framework for assessing nanomedicines and their off-patent counterparts, termed as nanosimilar, which could be considered as \'similar\' to the prototype nanomedicine,based on essential criteria describing the \'similarity\'. The term \'similarity\' should be focused on criteria that describe nanocarriers, encompassing their physicochemical, thermodynamic, morphological, and biological properties, including surface interactions and pharmacokinetics. Nanocarriers can be regarded as advanced self-assembled excipients (ASAEs) due to their complexity and chaotic behavior and should be evaluated by using essential criteria in order for off-patent nanomedicines be termed as nanosimilars, from a regulatory perspective. Collaboration between the pharmaceutical industry, regulatory bodies, and artificial intelligence (AI) startups is pivotal for the precise characterization and approval processes for nanomedicines and nanosimilars and embracing innovative tools and terminology facilitates the development of a sustainable regulatory framework, ensuring safety and efficacy. This crucial shift toward precision R&D practices addresses the complexity inherent in nanocarriers, paving the way for therapeutic advancements with economic benefits.

Predicting physical properties of complex multi-scale systems is a common challenge and demands analysis of various temporal and spatial scales. However, physics alone is often not sufficient due to lack of knowledge on certain details of the system. With sufficient data, however, machine learning techniques may aid. If data are yet relatively cumbersome to obtain, hybrid methods may come to the rescue. We focus in this report on using various types of neural networks (NN) including NN\'s into which physics information is encoded (PeNN\'s) and also studied effects of NN\'s hyperparameters. We apply the networks to predict the viscosity of an emulsion as a function of shear rate. We show that using various network performance metrics as the mean squared error and the coefficient of determination ( R 2 ) that the PeNN\'s always perform better than the NN\'s, as also confirmed by a Friedman test with a p-value smaller than 0.0002. The PeNN\'s capture extrapolation and interpolation very well, contrary to the NN\'s. In addition, we have found that the NN\'s hyperparameters including network complexity and optimization methods do not have any effect on the above conclusions. We suggest that encoding NN\'s with any disciplinary system based information yields promise to better predict properties of complex systems than NN\'s alone, which will be in particular advantageous for small numbers of data. Such encoding would also be scalable, allowing different properties to be combined, without repetitive training of the NN\'s.

This chapter of the New York City Panel on Climate Change 4 (NPCC4) report discusses the many intersecting social, ecological, and technological-infrastructure dimensions of New York City (NYC) and their interactions that are critical to address in order to transition to and secure a climate-adapted future for all New Yorkers. The authors provide an assessment of current approaches to \"future visioning and scenarios\" across community and city-level initiatives and examine diverse dimensions of the NYC urban system to reduce risk and vulnerability and enable a future-adapted NYC. Methods for the integration of community and stakeholder ideas about what would make NYC thrive with scientific and technical information on the possibilities presented by different policies and actions are discussed. This chapter synthesizes the state of knowledge on how different communities of scholarship or practice envision futures and provides brief descriptions of the social-demographic and housing, transportation, energy, nature-based, and health futures and many other subsystems of the complex system of NYC that will all interact to determine NYC futures.

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network-based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data-driven approaches for the hazard assessment of nanomaterials and other advanced materials.

Ever since Varela and Maturana proposed the concept of autopoiesis as the minimal requirement for life, there has been a focus on cellular systems that erect topological boundaries to separate themselves from their surrounding environment. Here, we reconsider whether the existence of such a spatial boundary is strictly necessary for self-producing entities. This work presents a novel computational model of a minimal autopoietic system inspired by dendrites and molecular dynamic simulations in three-dimensional space. A series of simulation experiments where the metabolic pathways of a particular autocatalytic set are successively inhibited until autocatalytic entities that could be considered autopoietic are produced. These entities maintain their distinctness in an environment containing multiple identical instances of the entities without the existence of a topological boundary. This gives rise to the concept of a metabolic boundary which manifests as emergent self-selection criteria for the processes of self-production without any need for unique identifiers. However, the adoption of such a boundary comes at a cost, as these autopoietic entities are less suited to their simulated environment than their autocatalytic counterparts. Finally, this work showcases a generalized metabolism-centered approach to the study of autopoiesis that can be applied to both physical and abstract systems alike.

The brain\'s complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.