While the control of cell migration by biochemical and biophysical factors is largely documented, a precise quantification of cell migration parameters in different experimental contexts is still questionable. Indeed, these phenomenological parameters can be evaluated from data obtained either at the cell population level or at the individual cell level. However, the range within which both characterizations of cell migration are equivalent remains unclear. We analyse here to which extent both sources of data could be integrated within a unified description of cell migration by considering the motility of the endothelial cell line EAhy926. Using time-lapse video-microscopy and associated analysis of digital image time series, we quantified EAhy926 random motility coefficient, migration speed and trajectory persistence time in two different migration assays: the in vitro wound healing assay, and the cell-populated agarose drop assay. In order to analyse the agreement between independent quantifications of cell motility based either on individual cell analysis or cell population dynamic analysis, a theoretical multi-agents cellular model was developed and discussed as a possible theoretical framework able to unify these multi-scale data. Model simulations especially reveal the potential bias induced by cell proliferation and cell-cell adhesion when cell migration parameters are estimated from the extensively used in vitro wound healing assay.
River Basin models to inform planning decisions have continued to evolve, largely based on predominant planning paradigms and progress in the sciences and technology. From the Industrial Revolution to the first quarter of the 21st century, such modeling tools have shifted from supporting water resources development to integrated and adaptive water resources management. To account for the increasing complexity and uncertainty associated with the relevant socioecological systems in which planning should be embedded, river basin models have shifted from a supply development focus during the 19th century to include, by thes 2000s–2020s, demand management approaches and all aspects of consumptive and non-consumptive uses, addressing sociocultural and environmental issues. With technological and scientific developments, the modeling has become increasingly quantitative, integrated and interdisciplinary, attempting to capture, more holistically, multiple river basin issues, relevant cross-sectoral policy influences, and disciplinary perspectives. Additionally, in acknowledging the conflicts around ecological degradation and human impacts associated with intensive water resource developments, the modeling has matured to embrace the need for adequate stakeholder engagement processes that support knowledge-sharing and trust-building and facilitate the appreciation of trade-offs across multiple types of impacts and associated uncertainties. River basin models are now evolving to anticipate uncertainty around plausible alternative futures such as climate change and rapid sociotechnical transformations. The associated modeling now embraces the challenge of shifting from predictive to exploratory tools to support learning and reflection and better inform adaptive management and planning. Managing so-called deep uncertainty presents new challenges for river basin modeling associated with imperfect knowledge, integrating sociotechnical scales, regime shifts and human factors, and enabling collaborative modeling, infrastructure support, and management systems.
Context: Ferralsols, which cover approximately 6% of the Earth's continental surface, have unique phosphorus (P) retention properties. Aims:The purpose of the research is to investigate P adsorption properties under noncontrolled conditions using lateritic soil samples, using wet chemical experiments, microscopic, and infrared spectroscopic methods.Methods: Ferralsol samples were analyzed using: 1) kinetic and adsorption isotherms (wet chemical experiment methods), 2) electron microscopy (SEM, TEM) and 3) infrared spectroscopy (ATR-FTIR). Key results:Wet chemical experiments accord with previous studies on lateritic soils where chemisorption mechanisms govern P adsorption. Further, P adsorption itself affect soil Investigating Phosphate Adsorption Mechanisms 2 particles' crystal structure by reductive dissolution of the Fe oxyhydroxide lattice. SEM investigation revealed the location of phosphate-surface complexes in iron-rich areas on soil particles. Infrared spectroscopy (ATR-FTIR) indicated the formation of two inner-sphere complexes: monodentate (FeO)PO2(OH) and bidentate (FeO)2PO(OH) at wavenumber positions 958±5, 1042±5 and 1095±8 cm -1 and 930±5, 983±10, 1005±5 and 1122±9 cm -1 , respectively. Additionally, for the bidentate complex, a band centred at 1030±4 was identified for P concentrations above 500 mgP/L, potentially indicative of a ternary complex. Combined methods suggested the potential involvement of redox mechanisms and other ionic species on the formation and types of P surface complexes.Conclusions: Our approach builds on previous work in this field by showing evidence of complex ionic interactions governing P retention on lateritic soils. Novel insights are evidence of fluctuations in physical and chemical factors with phosphate concentration in solution and adsorption, and suggestion of inner-sphere and ternary surface complexation mechanisms.Implications: Given the wide global distribution of lateritic Ferralsols, our findings have important implications for key emerging challenges relating to P cycling for crop production and environmental impact.
Integrated Assessment Models (IAMs) were initially developed to inform decision processes relating to climate change and then extended to other natural resource management decisions, including issues around integrated water resources management. Despite their intention to support long-term planning decisions, model uptake has generally been limited, partly due to their unfulfilled capability to manage deep uncertainty issues and consider multiple perspectives and trade-offs involved when solving problems of interest. In recent years, more emphasis has been put on the need for existing models to evolve to be used for exploratory modeling and analysis to capture and manage deep uncertainty. Building new models is a solution but may face challenges in terms of feasibility and the conservation of knowledge assets. Integration and augmentation of existing models is another solution, but little guidance exists on how to realize model augmentation that addresses deep uncertainty and how to use such models for exploratory modeling purposes. To provide guidance on how to augment existing models to support decisions under deep uncertainty we present an approach for identifying minimum information requirements (MIRs) that consists of three steps: (1) invoking a decision support framework [here, Dynamic Adaptive Policy Pathways (DAPP)] to synthesize information requirements, (2) characterizing misalignment with an existing integrated model, (3) designing adjustable solutions that align model output with immediate information needs. We employ the Basin Futures model to set up the approach and illustrate outcomes in terms of its effectiveness to augment models for exploratory purposes, as well as its potential for supporting the design of adaptative pathways. The results are illustrated in the context of the Brahmani River Basin (BRB) system and discussed in terms of generalization and transferability of the approach to identifying MIRs. Future work directions include the refinement and evaluation of the approach in a planning context and testing of the approach with other models.
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