Scale Trajectory Research Articles

Disability trajectories by progression independent of relapse activity status differ in pediatric, adult and late-onset multiple sclerosis

BackgroundTo compare Expanded Disability Status Scale (EDSS) trajectories over time between Multiple Sclerosis (MS) groups with pediatric (POMS), adult (AOMS) and late (LOMS) onset, and between patients with and without progression independent of relapse activity (PIRA).MethodsPatients with a first visit within 1 year from onset, ≥ 5-year follow-up and ≥ 1 visit every 6 months were selected from the Italian MS Register. Adjusted disability trajectories were assessed by longitudinal models for repeated measures. Comparisons between groups and between patients with and without PIRA in subgroups were performed by evaluating the yearly differences of mean EDSS score changes versus baseline (delta-EDSS).A first CDA event was defined as a 6-months confirmed disability increase from study baseline, measured by EDSS (increase ≥ 1.5 points with baseline EDSS = 0; ≥ 1.0 with baseline EDSS score ≤ 5.0 and ≥ 0.5 point with baseline EDSS > 5.5).PIRA was defined as a CDA event occurring more than 90 days after and more than 30 days before the onset of a relapse.Results3777 MS patients (268 POMS, 3282 AOMS, 227 LOMS) were included. The slope of disability trajectories significantly diverged in AOMS vs POMS starting from the second year of follow-up (Year 2: delta2-EDSS 0.18 (0.05; 0.31), p = 0.0054) and then mean delta2-EDSS gradually increased up to 0.23 (0.07; 0.39, p = 0.004) at year 5. Patients with PIRA had significant (p < 0.0001) steeper increase in EDSS scores than those without PIRA in all groups, although in POMS, the disability trajectories began to diverge later and at a lesser extent with delta-EDSS score of 0.48 vs 0.83 in AOMS and 1.57 in LOMS, at 3 years after the first PIRA.ConclusionsAge is relevant in determining disability progression in MS. POMS shows a less steep increase in EDSS scores over time than older patients. The effect of PIRA in accelerating EDSS progression is less pronounced in POMS than in AOMS and LOMS.

Computational Analysis of Activation of Dimerized Epidermal Growth Factor Receptor Kinase Using the String Method and Markov State Model.

Epidermal growth factor receptor (EGFR) activation is accompanied by dimerization. During the activation of the intracellular kinase domain, two EGFR kinases form an asymmetric dimer, and one side of the dimer (receiver) is activated. Using the string method and Markov state model (MSM), we performed a computational analysis of the structural changes in the activation of the EGFR dimer in this study. The string method reveals the minimum free-energy pathway (MFEP) from the inactive to active structure. The MSM was constructed from numerous trajectories of molecular dynamics simulations around the MFEP, which revealed the free-energy map of structural changes. In the activation of the receiver kinase, the unfolding of the activation loop (A-loop) is followed by the rearrangement of the C-helix, as observed in other kinases. However, unlike other kinases, the free-energy map of EGFR at the asymmetric dimer showed that the active state yielded the highest stability and revealed how interactions at the dimer interface induced receiver activation. As the H-helix of the activator approaches the C-helix of the receiver during activation, the A-loop unfolds. Subsequently, L782 of the receiver enters the pocket between the G- and H-helices of the activator, leading to a rearrangement of the hydrophobic residues around L782 of the receiver, which constitutes a structural rearrangement of the C-helix of the receiver from an outward to an inner position. The MSM analysis revealed long-time scale trajectories via kinetic Monte Carlo.

Designing an instrument for scaling public sector innovations

Abstract Governments worldwide invest in developing and diffusing innovations to deal with wicked problems. While experiments and pilots flourish, governments struggle to successfully scale innovations. Public sector scaling remains understudied, and scholarly suggestions for scaling trajectories are lacking. Following a design approach, this research develops an academically grounded, practice-oriented scaling instrument for planning and reflecting on the scaling of public sector innovations. We design this instrument based on the academic literature, an empirical analysis of three scaling projects at the Dutch Ministry of Infrastructure and Water Management, and six focus groups with practitioners. This research proposes a context-specific and iterative understanding of scaling processes and contributes a typology of scaling barriers and an additional scaling strategy to the literature. The presented instrument increases our academic understanding of scaling and enables teams of policymakers, in cooperation with stakeholders, to plan and reflect on a context-specific scaling pathway for public sector innovations.

Forest demography and biomass accumulation rates are associated with transient mean tree size vs. density scaling relations.

Linking individual and stand-level dynamics during forest development reveals a scaling relationship between mean tree size and tree density in forest stands, which integrates forest structure and function. However, the nature of this so-called scaling law and its variation across broad spatial scales remain unquantified, and its linkage with forest demographic processes and carbon dynamics remains elusive. In this study, we develop a theoretical framework and compile a broad-scale dataset of long-term sample forest stands (n = 1,433) from largely undisturbed forests to examine the association of temporal mean tree size vs. density scaling trajectories (slopes) with biomass accumulation rates and the sensitivity of scaling slopes to environmental and demographic drivers. The results empirically demonstrate a large variation of scaling slopes, ranging from -4 to -0.2, across forest stands in tropical, temperate, and boreal forest biomes. Steeper scaling slopes are associated with higher rates of biomass accumulation, resulting from a lower offset of forest growth by biomass loss from mortality. In North America, scaling slopes are positively correlated with forest stand age and rainfall seasonality, thus suggesting a higher rate of biomass accumulation in younger forests with lower rainfall seasonality. These results demonstrate the strong association of the transient mean tree size vs. density scaling trajectories with forest demography and biomass accumulation rates, thus highlighting the potential of leveraging forest structure properties to predict forest demography, carbon fluxes, and dynamics at broad spatial scales.

GeoPM-DMEIRL: A deep inverse reinforcement learning security trajectory generation framework with serverless computing

Vehicle trajectory data is essential for traffic management and location-based services. However, the release of trajectories raises serious privacy concerns because they contain sensitive information, such as home addresses and workplaces, etc, making it indispensable to consider privacy protection when releasing trajectory data. It is urgent to reconcile data utility with privacy in trajectory generation calls for advanced methods that adhere to real-world traffic rules. Leveraging serverless computing for deep learning provides an efficient solution, bypassing infrastructure complexities to handle the scale trajectory data. In this paper, a Geo-Piecewise Mechanism Deep Maximum Entropy Inverse Reinforcement Learning (GeoPM-DMEIRL) is proposed for secure trajectory generation, which consists of three key components, namely GeoPM, A2C reinforcement learning and DMEIRL. Firstly, GeoPM is a novel local differential privacy mechanism that incorporates Geo-aware gridding techniques. The Piecewise Mechanism is used to perturb the user trajectory while ensuring that the perturbed trajectory conforms to real-world traffic rules. Secondly, an A2C reinforcement learning network is refined to train the optimal trajectory generation strategy. Thirdly, Deep Maximum Entropy Inverse Reinforcement Learning is improved to train the weights of the A2C reward function. Finally, real-world data are collected in the experiments and the serverless computing platform AWS Lambda is used for training the reinforcement learning models. Experimental results show that our proposed GeoPM-DMEIRL framework can effectively resist user re-identification attacks, which can improve the utility by an average of 54.605% and enhance the privacy by an average of 30.678%. Meanwhile, GeoPM-DMEIRL is able to maintain the utility of the data while protecting the privacy of the user’s trajectories.

C-level managers and born-digitals' scaling: The case of Initial Coin Offerings (ICOs)

This research contributes to the Management of Technology (MOT) literature by scrutinizing the interrelation between education, experience, and the scaling aptitudes of high-tech companies. The study hinges on a comprehensive analysis of data collected from 1987 C-level executives and 3644 born-digital firms that pursued funding via blockchain-based Initial Coin Offerings (ICOs). Employing structural equation modeling, we systematically tested our hypotheses, contrasting the scaling trajectories of companies that successfully procured funding against those that fell short. Our findings reveal that amongst the diverse managerial competencies, only the leadership acumen of CEOs plays an important role in fostering the scaling of tech-companies across the spectrum, leaving the proficiencies of CFOs and CTOs with negligible impact. More crucially, the competencies of a CEO magnify in importance in relation to a tech-company's scaling potential post the securing of funds. The insights gained from this study not only enrich the existing body of knowledge on scaling and ICOs within the MOT literature but also hold considerable practical value for crafting effective scaling strategies in the high-tech industry.

CO199 Development of a Novel Transdiagnostic Predictor of Psychiatric Hospitalization Using Early Trajectories of the Clinical Global Impression Scale

Existing predictors of psychiatric hospitalization are often limited to specific diagnoses or clinical scenarios. This study proposed an operationalization of clinical instability based on individual-level in Clinical Global Impression-Severity (CGI-S) scores and investigated its association with risk of psychiatric hospitalization.

Trajectories of neighborhood environmental factors and their associations with asthma symptom trajectories among children in Australia: evidence from a national birth cohort study

PurposeThis study aims to investigate the prospective associations of neighborhood environmental exposure trajectories with asthma symptom trajectories during childhood developmental stages.MethodsWe considered asthma symptom, neighborhood environmental factors, and socio-demographic data from the “Longitudinal Study of Australian Children (LSAC)”. Group-based trajectory modeling was applied to identify the trajectories of asthma symptom, neighborhood traffic conditions, and neighborhood livability scales (considered for safety and facilities). We used multivariable logistic regression models to assess associations between various neighborhood environmental factors and asthma symptom trajectories.ResultsWe included 4,174 children from the LSAC cohort in our study. Three distinct trajectories for asthma symptom were the outcome variables of this study. Among the neighborhood environmental factors, we identified two distinct trajectories for the prevalence of heavy traffic on street, and two trajectories of neighborhood liveability scale. Compared to the ‘Low/no’ asthma symptoms trajectory group, children exposed to a ‘persistently high’ prevalence of heavy traffic on street was also significantly associated with both ‘transient high’ [relative risk ratio (RRR):1.40, 95% CI:1.25,1.58) and ‘persistent high’ (RRR: 1.33, 95% CI:1.17,1.50)] asthma symptom trajectory groups. Trajectory of moderate and static neighborhood liveability score was at increased risk of being classified as ‘transient high’ (RRR:1.16, 95% CI:1.07,1.25) and ‘persistent high’ (RRR:1.38, 95% CI:1.27,1.50) trajectories of asthma symptom.ConclusionExposure to heavy traffic and poor neighborhood liveability increased the risk of having an unfavourable asthma symptom trajectory in childhood. Reducing neighborhood traffic load and improving neighborhood safety and amenities may facilitate a favorable asthma symptom trajectory among these children.

Uncertainty and Strategic Entrepreneurial Action- Scaling Patterns in Technology-Based New Ventures

By combining strategic and entrepreneurial action theories, we consider scaling as strategic entrepreneurial action and aim to explain how TMT's choice to deal with uncertainty and its two relevant knowledge problems enables them to design scaling patterns. To do so, we adopt an information processing perspective to investigate the uncertainty-relevant knowledge problems posed to TMT of ten technology-based new ventures throughout their successful and unsuccessful scaling efforts in a business setting wherein uncertainty is at its most extreme and imposed on firms by multiple sources. Our findings demonstrate that TMT's approach to dealing with uncertainty and relevant knowledge problems leads the firm to follow two different scaling paths: spiral and dynamic embedding. Spiral refers to a scaling trajectory in which the firm avoids dealing with high environmental uncertainties and chooses to switch between potential markets by adapting to existing environmental structures. In contrast, dynamic embedding refers to a scaling path wherein the firm tends to deal with high environmental uncertainty and chooses to engage in shaping new environmental arrangements to embed its technologies. We also show that spiral scaling is made through endogenously dynamic capabilities, and dynamic embedding scaling is produced through exogenously dynamic capabilities. We contribute to the literature by providing a deeper understanding of when and how strategic actors scale up the firm under uncertainty, as well as the origin of dynamic capabilities required for scaling in technology-based new ventures.

Essaying the Impasse

Essaying the Impasse

Towards a Science of Scaling for Urban Climate Action and Governance

AbstractThe scaling of urban climate action and its governance is rapidly becoming a central focus in the urban climate governance literature and policy debates. Building on the broader scaling literature and inspired by related initiatives in other fields, this article calls for the development of a systematic “science of scaling” for urban climate governance. Such a science of scaling may help to give a better understanding of how well-performing urban climate action and its governance can be multiplied, accelerated and broadened (ie horizontal and vertical scaling and scaling out, up and down), and it may help to uncover scaling trajectories towards systemic change in cities (ie deep scaling).

Trait mindfulness scores are related to trajectories of depressive symptoms during pregnancy

BackgroundExploring possible protective factors against antenatal depression is important since antenatal depression is common and affects both mother and child. The person characteristic trait mindfulness may be such a protective factor. Because of the high variability in depressive symptoms over time, we aimed to assess the association between trait mindfulness and trajectories of depressive symptoms during pregnancy. MethodsA subsample of 762 women participating in the HAPPY study completed the Three Facet Mindfulness Questionnaire-Short Form at 22 weeks of pregnancy. Possible different trajectories of Edinburgh Postnatal Depression Scale (EPDS) scores, assessed at each pregnancy trimester, were explored with growth mixture modeling. ResultsThree EPDS trajectories (classes) were identified: low stable symptom scores (N = 607, 79.7%), decreasing symptom scores (N = 74, 9.7%) and increasing symptom scores (N = 81, 10.6%). Compared to belonging to the low stable class (reference), women with higher scores on the trait mindfulness facets ‘acting with awareness’ and ‘non-judging’ were less likely to belong to the decreasing class (OR = 0.81, 95% CI [0.73, 0.90] and OR = 0.77, 95% CI [0.70, 0.84]) and increasing class (OR = 0.88, 95% CI [0.80, 0.97] and OR = 0.72, 95% CI [0.65, 0.79]). Women with higher scores on ‘non-reacting’ were less likely to belong to the increasing class (OR = 0.89, 95% CI [0.82, 0.97]), but not the decreasing class (OR = 0.96, 95% CI [0.87, 1.04]). All analyses were adjusted for confounders. ConclusionsCharacteristics of trait mindfulness predicted low stable levels of depressive symptoms throughout pregnancy. Mindfulness-based programs may be beneficial for pregnant women as a strategy to alleviate depression risks.

Relationship between capillaries, mitochondria and maximum power of the heart: a meta-study from shrew to elephant.

This meta-study uses phylogenetic scaling models across more than 30 species, spanning five orders of magnitude in body mass, to show that cardiac capillary numerical density and mitochondrial volume density decrease with body mass raised to the -0.07 ± 0.03 and -0.04 ± 0.01 exponents, respectively. Thus, while an average 10 g mammal has a cardiac capillary density of approximately 4150 mm-2 and a mitochondrial density of 33%, a 1 t mammal has considerably lower corresponding values of 1850 mm-2 and 21%. These similar scaling trajectories suggest quantitative matching for the primary oxygen supply and oxygen consuming structures of the heart, supporting economic design at the cellular level of the oxygen cascade in this aerobic organ. These scaling trajectories are nonetheless somewhat shallower than the exponent of -0.11 calculated for the maximum external mechanical power of the cardiac tissue, under conditions of heavy exercise, when oxygen flow between capillaries and mitochondria is probably fully exploited. This mismatch, if substantiated, implies a declining external mechanical efficiency of the heart with increasing body mass, whereby larger individuals put more energy in but get less energy out, a scenario with implications for cardiovascular design, aerobic capacity and limits of body size.

Disentangling Challenges to Scaling Alternate Wetting and Drying Technology for Rice Cultivation: Distilling Lessons From 20 Years of Experience in the Philippines

Alternate wetting and drying (AWD) is a low-cost innovation that enables farmers to adapt to increasingly water scarcity conditions (such as drought), increase overall farm production efficiency, and mitigate greenhouse gas (GHG) emissions. It is seen as a pathway for transforming agri-food systems into more resilient, productive, biologically diverse, and equitable forms, ensuring our commitments to the UN Sustainable Development Goals (SDGs). This paper uses scaling up and innovation uncertainty frameworks to review the success and challenges of AWD's 20-year scaling trajectory in the Philippines and explain the key factors that have influenced its outcomes. The framework adapted for this study is also used to examine the fitness between the scaling context and requirements, organizational mission, and corresponding capabilities. Findings show the innovation platform that vertically integrated key actors and locally adapted AWD has helped foster essential breakthroughs in creating an enabling environment that took AWD to national policy adoption in the Philippines. However, the dominant focus on technology transfer, product focus, and preference for controlled environments in the scaling practice has neglected many important contextual factors, allowing mismatches in enabling policy incentives, institutions, and scale to diminish the impacts of AWD in gravity-based systems. Our findings suggest that rethinking and re-envisioning the ways in which the impact can be scaled in irrigation rice systems using AWD is critical to sustaining food security and making the agriculture sector more resilient to climate change.

Trajectories in early rheumatoid arthritis related fatigue over 10 years: results from the ESPOIR cohort.

In a cohort of early rheumatoid arthritis (RA) patients, we aimed to determine and characterise fatigue trajectories over 10 years of follow-up and identify predictors of trajectory membership. We selected patients fulfilling the 2010 ACR/EULAR criteria for RA included in the ESPOIR cohort. We used a cluster analysis to obtain fatigue (assessed by fatigue visual analogue scale) trajectories over the course of 10 years from enrolment. Chi-square tests or ANOVA were performed to evaluate differences of baseline variables between fatigue trajectories. Using a multinomial logistic regression we were able to identify predictors of trajectory membership. We analysed 598 patients with mean disease duration at enrolment of 26.2±40.9 days. Cluster analysis revealed 3 trajectories: high (18%), moderate (52%) and low fatigue (30%). Compared to patients with moderate or low fatigue trajectory, patients with high fatigue trajectory were predominantly women and reported significantly higher duration and intensity of morning stiffness, HAQ score, tender joints count, levels of pain, number of awakenings due to arthritis, frequency of fibromyalgic RA, levels of physician and patient global assessment, more frequent sleep problems, and increased psychological distress. Female patients with pain, psychological distress and presence of sicca symptoms had a higher risk of being in the high trajectory group. These findings suggest that levels of fatigue are rather stable over time in each trajectory. Baseline clinical measures and baseline patient-reported measures of functional status better distinguished the three fatigue trajectories. We did not find any differences between trajectories in baseline laboratory measures. Inflammatory activity was not a predictor of being in the high trajectory fatigue group.

Poor man’s scaling: XYZ Coqblin–Schrieffer model revisited

We derive the third-order poor man’s scaling equation for a generic Hamiltonian describing a quantum impurity embedded into an itinerant electron gas. We show that the XYZ Coqblin–Schrieffer model introduced by one of us earlier is algebraically renormalizable in the sense that the form of the Hamiltonian is preserved along the scaling trajectory, write down the scaling equations for the model, and analyze the renormalization group flows in the cases of both constant and pseudogap densities of states.

Inpatient COVID-19 outcomes in solid organ transplant recipients compared to non-solid organ transplant patients: A retrospective cohort.

Immunosuppression and comorbidities might place solid organ transplant (SOT) recipients at higher risk from COVID-19, as suggested by recent case series. We compared 45 SOT vs. 2427 non-SOT patients who were admitted with COVID-19 to our health-care system (March 1, 2020 - August 21, 2020), evaluating hospital length-of-stay and inpatient mortality using competing-risks regression. We compared trajectories of WHO COVID-19 severity scale using mixed-effects ordinal logistic regression, adjusting for severity score at admission. SOT and non-SOT patients had comparable age, sex, and race, but SOT recipients were more likely to have diabetes (60% vs. 34%, p<.001), hypertension (69% vs. 44%, p=.001), HIV (7% vs. 1.4%, p=.024), and peripheral vascular disorders (19% vs. 8%, p=.018). There were no statistically significant differences between SOT and non-SOT in maximum illness severity score (p=.13), length-of-stay (sHR: 0.9 1.11.4 , p=.5), or mortality (sHR: 0.1 0.41.6 , p=.19), although the severity score on admission was slightly lower for SOT (median [IQR] 3 [3, 4]) than for non-SOT (median [IQR] 4 [3-4]) (p=.042) Despite a higher risk profile, SOT recipients had a faster decline in disease severity over time (OR = 0.76 0.810.86 , p<.001) compared with non-SOT patients. These findings have implications for transplant decision-making during the COVID-19 pandemic, and insights about the impact of SARS-CoV-2 on immunosuppressed patients.

Can flexible timing of harvest for translocation reduce the impact on fluctuating source populations?

Abstract Context Species translocations are used in conservation globally. Although harvest for translocation may have negative impacts on source populations, translocation programs rarely explore ways of minimising those impacts. In fluctuating source populations, harvest timing may affect its impact because population size and trajectory vary among years. Aims We explored whether the timing and scale of harvest can be altered to reduce its impact on a fluctuating source population of Mallee Emu-wrens, Stipiturus mallee; an endangered passerine in south-eastern Australia. Mallee Emu-wren populations fluctuate with ~5–10-year drought–rain cycles. Methods We used population viability analysis (PVA) to compare the impact of five harvest scales (no harvest, 100, 200, 300 or 500 individuals) under three population trajectories (increasing, stable or decreasing) and two initial population sizes (our model-based estimate of the population size and the lower 95% confidence interval of that estimate). To generate a model-based estimate of the population size, we surveyed 540 sites (9 ha), stratified according to environmental variables known to affect Mallee Emu-wren occurrence. We used an information-theoretic approach with N-mixture models to estimate Mallee Emu-wren density, and extrapolated results over all potential habitat. Key Results We estimate that in spring 2019, the source population consisted of 6449 individuals, with a minimum of 1923 individuals (lower 95% confidence interval). Of 48 harvest scenarios, only seven showed no impact of harvest within 5 years (15%). Those seven all had increasing population trajectories and carrying capacity set to equal initial population size. Twenty-six populations showed no impact of harvest within 25 years (54%). These were either increasing populations that had reached carrying capacity or decreasing populations nearing extinction. Conclusions Initial population size, carrying capacity, harvest scale and population trajectory were all determinants of harvest impact. Given the importance of carrying capacity, further research is required to determine its role in the Mallee Emu-wren source population. Implications Harvesting Mallee Emu-wrens after high-rainfall years will have the least impact because source populations are likely to be large with increasing trajectories. For fluctuating source populations, flexibility in the timing of harvest can reduce its impact and should be considered during translocation planning.

Non-equilibrium landscape and flux reveal the stability-flexibility-energy tradeoff in working memory.

Uncovering the underlying biophysical principles of emergent collective computational abilities, such as working memory, in neural circuits is one of the most essential concerns in modern neuroscience. Working memory system is often desired to be robust against noises. Such systems can be highly flexible for adapting environmental demands. How neural circuits reconfigure themselves according to the cognitive task requirement remains unclear. Previous studies explored the robustness and the flexibility in working memory by tracing individual dynamical trajectories in a limited time scale, where the accuracy of the results depends on the volume of the collected statistical data. Inspired by thermodynamics and statistical mechanics in physical systems, we developed a non-equilibrium landscape and flux framework for studying the neural network dynamics. Applying this approach to a biophysically based working memory model, we investigated how changes in the recurrent excitation mediated by slow NMDA receptors within a selective population and mutual inhibition mediated by GABAergic interneurons between populations affect the robustness against noises. This is realized through quantifying the underlying non-equilibrium potential landscape topography and the kinetics of state switching. We found that an optimal compromise for a working memory circuit between the robustness and the flexibility can be achieved through the emergence of an intermediate state between the working memory states. An optimal combination of both increased self-excitation and inhibition can enhance the flexibility to external signals without significantly reducing the robustness to the random fluctuations. Furthermore, we found that the enhanced performance in working memory is supported by larger energy consumption. Our approach can facilitate the design of new network structure for cognitive functions with the optimal balance between performance and cost. Our work also provides a new paradigm for exploring the underlying mechanisms of many cognitive functions based on non-equilibrium physics.

Science of Scaling: Understanding and guiding the scaling of innovation for societal outcomes

This Editorial to the Special Issue “Science of Scaling: connecting the pathways of agricultural research and development for improved food, income and nutrition security” presents the framing, overview and analysis of 10 articles focussed on scaling innovation in the agricultural research for development sector. The publications cut across three categories that focus on: (i) Understanding the scaling trajectory retrospectively from a longer term, systems perspective, (ii) Understanding scaling of innovation retrospectively as part of shorter term agricultural research for development interventions, and (iii) Conceptual or methodological approaches aimed at guiding scaling prospectively. Cross-cutting review of the publications leads to several insights and critically questions dominant ways of understanding and guiding scaling of innovation in the agricultural research for development sector. This provides a starting point for proposing more outcome-oriented scaling as a third wave of understanding and guiding scaling, beyond technology adoption (first wave) and the scaling of innovation (second wave). The Editorial proposes three Research Domains for the Science of Scaling: (1) ‘Understand the big picture of scaling innovation’ that can inform more realistic ideas about the factors, conditions and dynamics that affect innovation and scaling processes; (2) ‘Develop instruments that nurture efficient and responsible scaling’ that comprises new approaches, concepts and tools that can facilitate the development of evidence-based scaling strategies; and (3) ‘Create a conducive environment for scaling innovation’ that focusses on the institutional arrangements, partnership models, and monitoring and learning for scaling of innovation.