Explorative and exploitative learning in project-based organizations: improving knowledge governance through a project management office?

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The role of co-creation in enhancing explorative and exploitative learning in project-based settings

We study how co-creation practices influence explorative and exploitative learning in five collaborative construction projects with partnering arrangements. Drawing on a longitudinal case study, our findings reveal two different types of explorative learning processes (i.e., adaptation and radical development) and three different exploitative learning processes (i.e., incremental development, knowledge sharing, and innovation diffusion). Furthermore, co-creation practices enhance adaptation, radical development, and incremental development, which are typical intra-project learning processes. Co-creation practices do not, however, enhance knowledge sharing and innovation diffusion across projects. These findings concur with previous insights that the temporary and one-off nature of projects makes inter-project learning problematic.published_or_final_versio

Image Quality Ranking Method for Microscopy

Automated analysis of microscope images is necessitated by the increased need for high-resolution follow up of events in time. Manually finding the right images to be analyzed, or eliminated from data analysis are common day-to-day problems in microscopy research today, and the constantly growing size of image datasets does not help the matter. We propose a simple method and a software tool for sorting images within a dataset, according to their relative quality. We demonstrate the applicability of our method in finding good quality images in a STED microscope sample preparation optimization image dataset. The results are validated by comparisons to subjective opinion scores, as well as five state-of-the-art blind image quality assessment methods. We also show how our method can be applied to eliminate useless out-of-focus images in a High-Content-Screening experiment. We further evaluate the ability of our image quality ranking method to detect out-of-focus images, by extensive simulations, and by comparing its performance against previously published, well-established microscopy autofocus metrics

Body surface area and glucose tolerance - The smaller the person, the greater the 2-hour plasma glucose

Background: The oral glucose tolerance test (OGTT) is standardized globally with a uniform glucose load of 75 g to all adults irrespective of body size. An inverse association between body height and 2-hour postload plasma glucose (2hPG) has been demonstrated. Our aim was to evaluate the relationship between body surface area (BSA) and plasma glucose values during an OGTT.Methods: An OGTT was performed on 2659 individuals at increased cardiovascular risk aged between 45 and 70 years of age, who had not previously been diagnosed with diabetes or cardiovascular disease. Their BSA was calculated according to the Mosteller formula. Study subjects were divided into five BSA levels corresponding to 12.5, 25, 25, 25, and 12.5% of the total distribution.Findings: When adjusted for age, sex, waist circumference, alcohol intake, current smoking, and leisure-time physical activity, BSA level showed an inverse linear relationship with the 2hPG in all categories of glucose tolerance (p for linearity Interpretation: Body size has a considerable impact on the findings from a standardized OGTT. Smaller persons are more likely to be diagnosed as glucose intolerant than relatively larger sized individuals.Research in context: Evidence before this study. We searched PubMed using the MeSH terms "glucose tolerance test", "body surface area", "body height", "body size", "glucose tolerance", "insulin resistance", "blood glucose" and "diabetes mellitus" on March 10, 2019 without language restrictions. We also used Cited Reference Search in Web of Science for relevant articles. The oral glucose tolerance test (OGTT) is standardized globally with a uniform glucose load of 75 g to all adults irrespective of body size. An inverse association between body height and 2-hour postload plasma glucose (2hPG) has been demonstrated. Several studies have shown that 2hPG predicts all-cause mortality better than elevated fasting glucose. However, body height or body surface area are not usually adjusted in epidemiological studies. It is well known that short adult stature is a risk factor for cardiovascular and all-cause mortality.Added value of this study. This is the first study to assess the relationship of body surface area and 2hPG in a typical primary care population at increased cardiovascular risk. Body surface area has a considerable impact on the result of a standardized OGTT. Smaller individuals are more likely to be diagnosed as glucose intolerant than relatively larger sized individuals.Implications of all the available evidence. There is a possibility that the diagnosis of type 2 diabetes made by an OGTT is a false positive result in a relatively small individual, and a false negative result in a relatively larger individual. Association of 2hPG concentrations and mortality may be influenced by body size as confounding factor. Given that the OGTT is a time and effort consuming test both for patients and laboratory personnel, validity of the OGTT for different body sizes should be reconsidered. (C) 2019 Elsevier B.V. All rights reserved.</div

Automated cell tracking using StarDist and TrackMate [version 1; peer review: awaiting peer review]

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images

Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.Comment: 13 pages, 5 tables, 15 figure

Automated cell tracking using StarDist and TrackMate

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images. </div

Co-opetition models for governing professional football

In recent years, models for co-creating value in a business-to-business context have often been examined with the aim of studying the strategies implemented by and among organisations for competitive and co-operative purposes. The traditional concepts of competition and co-operation between businesses have now evolved, both in terms of the sector in which the businesses operate and in terms of the type of goods they produce. Many researchers have, in recent times, investigated the determinants that can influence the way in which the model of co-opetition can be applied to the football world. Research interest lies in the particular features of what makes a good football. In this paper, the aim is to conduct an analysis of the rules governing the “football system”, while also looking at the determinants of the demand function within football entertainment. This entails applying to football match management the co-opetition model, a recognised model that combines competition and co-operation with the view of creating and distributing value. It can, therefore, be said that, for a spectator, watching sport is an experience of high suspense, and this suspense, in turn, depends upon the degree of uncertainty in the outcome. It follows that the rules ensuring that both these elements can be satisfied are a fertile ground for co-operation between clubs, as it is in the interest of all stakeholders to offer increasingly more attractive football, in comparison with other competing products. Our end purpose is to understand how co-opetition can be achieved within professional football

Human lactobacilli as supplementation of clindamycin to patients with bacterial vaginosis reduce the recurrence rate; a 6-month, double-blind, randomized, placebo-controlled study

<p>Abstract</p> <p>Background</p> <p>The primary objective of this study was to investigate if supplementary lactobacilli treatment could improve the initial cure rate after vaginal clindamycin therapy, and secondly, if lactobacilli as repeated adjunct treatment during 3 menstrual cycles could lengthen the time to relapse after initial cure.</p> <p>Methods</p> <p>Women (n = 100) with bacterial vaginosis diagnosed by Amsel criteria were after informed consent offered vaginal clindamycin therapy followed by vaginal gelatine capsules containing either 10⁹freeze-dried lactobacilli or identical placebo capsules for 10 days during 3 menstrual cycles in a double-blind, randomized, placebo-controlled trial.</p> <p>Results</p> <p>The initial intent to treat (ITT) analysis for the one-month cure rate was 64% in the lactobacilli group and 78% in the placebo group (p > 0.05). However, any patient with missing or unclassified smears at the initial visit who continued the study and whose next smear indicated a cure was included in the cured group; the study also excluded two of the patients in the lactobacilli group who reported that they did not take any vaginal capsules. With consideration to these population changes, the initial cure rate would be 77% in the lactobacilli group. The 76 cured women were followed for 6 menstrual cycles or until relapse within that time span. At the end of the study, 64.9% (24/37) of the lactobacilli treated women were still BV-free compared to 46.2% (18/39) of the placebo treated women. Comparison of the two groups regarding "Time from cure to relapse" was statistically significant (p = 0.027) in favour of the lactobacilli treatment. Adjuvant therapy with lactobacilli contributed significantly to avoidance of relapse with a proportional Hazard Risk ratio (HR) of 0.73 (0.54–0.98) (p < 0.05)</p> <p>Conclusion</p> <p>The study shows that supplementary treatment combining two different strains of probiotic lactobacilli does not improve the efficacy of BV therapy during the first month of treatment, but for women initially cured, adjunct treatment of lactobacilli during 3 menstrual cycles lengthens the time to relapse significantly in that more women remained BV free at the end of the 6-month follow up.</p> <p>Trial registration number</p> <p>ISRCTN62879834</p