Advancing the circular economy through dynamic capabilities and extended customer engagement: insights from small sustainable fashion enterprises in the UK

The circular economy holds the potential to significantly reduce resource use. However, attempts to fully utilize its potential have fallen short so far. Based on a longitudinal interview-based study, we examine how micro, small and medium enterprises (MSMEs) in the UK fashion industry advance the circular economy (CE). Whereas the dynamic capabilities framework is mostly used for medium and large businesses, our findings advance the current literature, demonstrating how the distinctive development and use of dynamic capabilities enable MSMEs to act in agile ways, allowing them to introduce, test and advance CE solutions, while providing them with more resilience during times of crises. Our study further shows that fashion MSMEs adopt circular economy business models (CEBMs) by going beyond conventional, technology-focused approaches currently dominating business thinking. The research highlights MSMEs' ability to engage in circular practices through an extension of existing business models in the form of close interactions with their customers demonstrating the importance and potential of extended business-customer engagement in businesses' attempts to adopt CE practices

The role and potential of tripartite partnerships to promote strong sustainable consumption in the context of Brazil: an evaluation of possibilities and risks

The growing concern about persisting environmental problems caused by overconsumption in the context of Brazil must be understood as an issue of democratic character. However, there is a gap in research examining models that can drive change of sustainable-related issues such as sustainable consumption. Critically evaluating existing literature, we discuss the potential of tripartite partnerships (TPPs) to advance sustainable consumption practices. We argue that multi-sector partnership approaches such as TPPs involving multiple actors can strengthen a socio-political basis for the advancement of public policies and inter-sectorial dynamics offering mechanisms that can foster sustainable consumption. By applying a TPP model as analytical lens, we explore prevalent possibilities and risks of promoting sustainable consumption in the context of Brazil

M-CSF and GM-CSF Regulation of STAT5 Activation and DNA Binding in Myeloid Cell Differentiation is Disrupted in Nonobese Diabetic Mice

Defects in macrophage colony-stimulating factor (M-CSF) signaling disrupt myeloid cell differentiation in nonobese diabetic (NOD) mice, blocking myeloid maturation into tolerogenic antigen-presenting cells (APCs). In the absence of M-CSF signaling, NOD myeloid cells have abnormally high granulocyte macrophage colony-stimulating factor (GM-CSF) expression, and as a result, persistent activation of signal transducer/activator of transcription 5 (STAT5). Persistent STAT5 phosphorylation found in NOD macrophages is not affected by inhibiting GM-CSF. However, STAT5 phosphorylation in NOD bone marrow cells is diminished if GM-CSF signaling is blocked. Moreover, if M-CSF signaling is inhibited, GM-CSF stimulation in vitro can promote STAT5 phosphorylation in nonautoimmune C57BL/6 mouse bone marrow cultures to levels seen in the NOD. These findings suggest that excessive GM-CSF production in the NOD bone marrow may interfere with the temporal sequence of GM-CSF and M-CSF signaling needed to mediate normal STAT5 function in myeloid cell differentiation gene regulation

Spatio-temporal correlations can drastically change the response of a MAPK pathway

Multisite covalent modification of proteins is omnipresent in eukaryotic cells. A well-known example is the mitogen-activated protein kinase (MAPK) cascade, where in each layer of the cascade a protein is phosphorylated at two sites. It has long been known that the response of a MAPK pathway strongly depends on whether the enzymes that modify the protein act processively or distributively: distributive mechanism, in which the enzyme molecules have to release the substrate molecules in between the modification of the two sites, can generate an ultrasensitive response and lead to hysteresis and bistability. We study by Green's Function Reaction Dynamics, a stochastic scheme that makes it possible to simulate biochemical networks at the particle level and in time and space, a dual phosphorylation cycle in which the enzymes act according to a distributive mechanism. We find that the response of this network can differ dramatically from that predicted by a mean-field analysis based on the chemical rate equations. In particular, rapid rebindings of the enzyme molecules to the substrate molecules after modification of the first site can markedly speed up the response, and lead to loss of ultrasensitivity and bistability. In essence, rapid enzyme-substrate rebindings can turn a distributive mechanism into a processive mechanism. We argue that slow ADP release by the enzymes can protect the system against these rapid rebindings, thus enabling ultrasensitivity and bistability

Molecular Dynamics Simulations of Cellulose and Dialcohol Cellulose under Dry and Moist Conditions

The development of wood-based thermoplastic polymers that can replace synthetic plastics is of high environmental importance, and previous studies have indicated that cellulose-rich fiber containing dialcohol cellulose (ring-opened cellulose) is a very promising candidate material. In this study, molecular dynamics simulations, complemented with experiments, were used to investigate how and why the degree of ring opening influences the properties of dialcohol cellulose, and how temperature and presence of water affect the material properties. Mechanical tensile properties, diffusion/mobility-related properties, densities, glass-transition temperatures, potential energies, hydrogen bonds, and free volumes were simulated for amorphous cellulosic materials with 0-100% ring opening, at ambient and high (150 \ub0C) temperatures, with and without water. The simulations showed that the impact of ring openings, with respect to providing molecular mobility, was higher at high temperatures. This was also observed experimentally. Hence, the ring opening had the strongest beneficial effect on “processability” (reduced stiffness and strength) above the glass-transition temperature and in wet conditions. It also had the effect of lowering the glass-transition temperature. The results here showed that molecular dynamics is a valuable tool in the development of wood-based materials with optimal thermoplastic properties

Geometry-controlled kinetics

It has long been appreciated that transport properties can control reaction kinetics. This effect can be characterized by the time it takes a diffusing molecule to reach a target -- the first-passage time (FPT). Although essential to quantify the kinetics of reactions on all time scales, determining the FPT distribution was deemed so far intractable. Here, we calculate analytically this FPT distribution and show that transport processes as various as regular diffusion, anomalous diffusion, diffusion in disordered media and in fractals fall into the same universality classes. Beyond this theoretical aspect, this result changes the views on standard reaction kinetics. More precisely, we argue that geometry can become a key parameter so far ignored in this context, and introduce the concept of "geometry-controlled kinetics". These findings could help understand the crucial role of spatial organization of genes in transcription kinetics, and more generally the impact of geometry on diffusion-limited reactions.Comment: Submitted versio

Bayesian inference of biochemical kinetic parameters using the linear noise approximation

Background Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data. Results We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo. Conclusion The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods

Regulatory control and the costs and benefits of biochemical noise

Experiments in recent years have vividly demonstrated that gene expression can be highly stochastic. How protein concentration fluctuations affect the growth rate of a population of cells, is, however, a wide open question. We present a mathematical model that makes it possible to quantify the effect of protein concentration fluctuations on the growth rate of a population of genetically identical cells. The model predicts that the population's growth rate depends on how the growth rate of a single cell varies with protein concentration, the variance of the protein concentration fluctuations, and the correlation time of these fluctuations. The model also predicts that when the average concentration of a protein is close to the value that maximizes the growth rate, fluctuations in its concentration always reduce the growth rate. However, when the average protein concentration deviates sufficiently from the optimal level, fluctuations can enhance the growth rate of the population, even when the growth rate of a cell depends linearly on the protein concentration. The model also shows that the ensemble or population average of a quantity, such as the average protein expression level or its variance, is in general not equal to its time average as obtained from tracing a single cell and its descendants. We apply our model to perform a cost-benefit analysis of gene regulatory control. Our analysis predicts that the optimal expression level of a gene regulatory protein is determined by the trade-off between the cost of synthesizing the regulatory protein and the benefit of minimizing the fluctuations in the expression of its target gene. We discuss possible experiments that could test our predictions.Comment: Revised manuscript;35 pages, 4 figures, REVTeX4; to appear in PLoS Computational Biolog

Diffusion of transcription factors can drastically enhance the noise in gene expression

We study by simulation the effect of the diffusive motion of repressor molecules on the noise in mRNA and protein levels in the case of a repressed gene. We find that spatial fluctuations due to diffusion can drastically enhance the noise in gene expression. For a fixed repressor strength, the noise due to diffusion can be minimized by increasing the number of repressors or by decreasing the rate of the open complex formation. We also show that the effect of spatial fluctuations can be well described by a two-step kinetic scheme, where formation of an encounter complex by diffusion and the subsequent association reaction are treated separately. Our results also emphasize that power spectra are a highly useful tool for studying the propagation of noise through the different stages of gene expression.Comment: 15 pages, 6 figures, REVTeX