Harnessing the Creative Potential of Consumers: Money, Participation and Creativity in Idea Crowdsourcing

Given the growing importance of innovation and consumer engagement, many firms are strongly interested in finding ways to encourage their consumers to generate creative new product ideas for them in their crowdsourcing initiatives. To that end, managers often use monetary rewards – one of the most commonly used managerial tools to stimulate desired behaviors. A critical question in this respect is whether the use of monetary rewards is effective in stimulating creativity and, if so, how large those rewards should be. This study aims to answer these questions. The results of an experiment suggest that introducing monetary rewards does not contribute to the number of new product ideas generated by a single consumer or the novelty of his/her ideas, and when the reward is relatively small it can even be harmful. Monetary rewards, however, are effective in encouraging widespread participation in crowdsourcing initiatives and improving the appropriateness of the new product ideas. As a whole, these findings take us a step further toward better understanding the motivational mechanisms of consumer creativity in new product ideation

Pulsed Feedback Defers Cellular Differentiation

Environmental signals induce diverse cellular differentiation programs. In certain systems, cells defer differentiation for extended time periods after the signal appears, proliferating through multiple rounds of cell division before committing to a new fate. How can cells set a deferral time much longer than the cell cycle? Here we study Bacillus subtilis cells that respond to sudden nutrient limitation with multiple rounds of growth and division before differentiating into spores. A well-characterized genetic circuit controls the concentration and phosphorylation of the master regulator Spo0A, which rises to a critical concentration to initiate sporulation. However, it remains unclear how this circuit enables cells to defer sporulation for multiple cell cycles. Using quantitative time-lapse fluorescence microscopy of Spo0A dynamics in individual cells, we observed pulses of Spo0A phosphorylation at a characteristic cell cycle phase. Pulse amplitudes grew systematically and cell-autonomously over multiple cell cycles leading up to sporulation. This pulse growth required a key positive feedback loop involving the sporulation kinases, without which the deferral of sporulation became ultrasensitive to kinase expression. Thus, deferral is controlled by a pulsed positive feedback loop in which kinase expression is activated by pulses of Spo0A phosphorylation. This pulsed positive feedback architecture provides a more robust mechanism for setting deferral times than constitutive kinase expression. Finally, using mathematical modeling, we show how pulsing and time delays together enable “polyphasic” positive feedback, in which different parts of a feedback loop are active at different times. Polyphasic feedback can enable more accurate tuning of long deferral times. Together, these results suggest that Bacillus subtilis uses a pulsed positive feedback loop to implement a “timer” that operates over timescales much longer than a cell cycle

Optimal (Randomized) Parallel Algorithms in the Binary-Forking Model

In this paper we develop optimal algorithms in the binary-forking model for a variety of fundamental problems, including sorting, semisorting, list ranking, tree contraction, range minima, and ordered set union, intersection and difference. In the binary-forking model, tasks can only fork into two child tasks, but can do so recursively and asynchronously. The tasks share memory, supporting reads, writes and test-and-sets. Costs are measured in terms of work (total number of instructions), and span (longest dependence chain). The binary-forking model is meant to capture both algorithm performance and algorithm-design considerations on many existing multithreaded languages, which are also asynchronous and rely on binary forks either explicitly or under the covers. In contrast to the widely studied PRAM model, it does not assume arbitrary-way forks nor synchronous operations, both of which are hard to implement in modern hardware. While optimal PRAM algorithms are known for the problems studied herein, it turns out that arbitrary-way forking and strict synchronization are powerful, if unrealistic, capabilities. Natural simulations of these PRAM algorithms in the binary-forking model (i.e., implementations in existing parallel languages) incur an $\Omega(\log n)$ overhead in span. This paper explores techniques for designing optimal algorithms when limited to binary forking and assuming asynchrony. All algorithms described in this paper are the first algorithms with optimal work and span in the binary-forking model. Most of the algorithms are simple. Many are randomized

A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli

Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alon

Simple methodology for the quantitative analysis of fatty acids in human red blood cells

In the last years, there has been an increasing interest in evaluating possible relations between fatty acid (FA) patterns and the risk for chronic diseases. Due to the long life span (120 days) of red blood cells (RBCs), their FA profile reflects a longer term dietary intake and was recently suggested to be used as an appropriate biomarker to investigate correlations between FA metabolism and diseases. Therefore, the aim of this work was to develop and validate a simple and fast methodology for the quantification of a broad range of FAs in RBCs using gas chromatography with flame ionization detector, as a more common and affordable equipment suitable for biomedical and nutritional studies including a large number of samples. For this purpose, different sample preparation protocols were tested and compared, including a classic two-step method (Folch method) with modifications and different one-step methods, in which lipid extraction and derivatization were performed simultaneously. For the one-step methods, different methylation periods and the inclusion of a saponification reaction were evaluated. Differences in absolute FA concentrations were observed among the tested methods, in particular for some metabolically relevant FAs such as trans elaidic acid and eicosapentaenoic acid. The one-step method with saponification and 60 min of methylation time was selected since it allowed the identification of a higher number of FAs, and was further submitted to in-house validation. The proposed methodology provides a simple, fast and accurate tool to quantitatively analyse FAs in human RBCs, useful for clinical and nutritional studies.This work received financial support from the European Union (FEDER funds through COMPETE) and National Funds (FCT, Fundação para a Ciência e Tecnologia) through project PTDC/SAU-ENB/116929/2010 and EXPL/EMS-SIS/2215/2013. ROR acknowledges PhD scholarship SFRH/BD/97658/2013 attributed by FCT (Fundação para a Ciência e Tecnologia).info:eu-repo/semantics/publishedVersio

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings

Systemic phenotype related to primary Sjögren's syndrome in 279 patients carrying isolated anti-La/SSB antibodies

Objective. To evaluate the systemic phenotype associated with the presence of isolated anti-La/SSB antibodies in a large international registry of patients with primary Sjögren's syndrome (pSS) fulfilling the 2002 classification criteria. Methods. The Big Data Sjögren Project Consortium is an international, multicentre registry created in 2014. Baseline clinical information from leading centres on clinical research in SS of the 5 continents was collected. Combination patterns of anti-Ro/SSA-La/SSB antibodies at the time of diagnosis defined the following four immu-nological phenotypes: Double positive (combined Ro/SSA and La/SSB,) isolated anti-Ro/SSA, isolated anti-La/ SSB, and immunonegative. Results. The cohort included 12,084 patients (11,293 females, mean 52.4 years) with recorded ESSDAI scores available. Among them, 279 (2.3%) had isolated anti-La/SSB antibodies. The mean total ESSDAI score at diagnosis of patients with pSS carrying isolated anti-La/SSB was 6.0, and 80.4% of patients had systemic activity (global ESSDAI score ≥ 1) at diagnosis. The domains with the highest frequency of active patients were the biological (42.8%), glandular (36.8%) and articular (31.2%) domains. Patients with isolated anti-La/ SSB showed a higher frequency of active patients in all ESSDAI domains but two (articular and peripheral nerve) in com-parison with immune-negative patients, and even a higher absolute frequency in six clinical ESSDAI domains in comparison with patients with isolated anti-Ro/ SSA. In addition, patients with isolated anti-La/SSB showed a higher frequency of active patients in two ESSDAI domains (pulmonary and glandular) with respect to the most active immunological subset (double-positive antibodies). Meanwhile, systemic activity detected in patients with isolated anti-La/SSB was overwhelmingly low. Even in ESSDAI domains where patients with isolated anti-La/SSB had the highest frequencies of systemic activity (lymphadenopathy and muscular), the percentage of patients with moderate or high activity was lower in comparison with the combined Ro/SSA and La/SSB group. Conclusion. Patients carrying isolated La/SSB antibodies represent a very small subset of patients with a systemic SS phenotype characterised by a significant frequency of active patients in most clinical ESSDAI domains but with a relative low frequency of the highest severe organ-specific involvements. Primary SS still remains the best clinical diagnosis for this subset of patients

Noise Management by Molecular Networks

Fluctuations in the copy number of key regulatory macromolecules (“noise”) may cause physiological heterogeneity in populations of (isogenic) cells. The kinetics of processes and their wiring in molecular networks can modulate this molecular noise. Here we present a theoretical framework to study the principles of noise management by the molecular networks in living cells. The theory makes use of the natural, hierarchical organization of those networks and makes their noise management more understandable in terms of network structure. Principles governing noise management by ultrasensitive systems, signaling cascades, gene networks and feedback circuitry are discovered using this approach. For a few frequently occurring network motifs we show how they manage noise. We derive simple and intuitive equations for noise in molecule copy numbers as a determinant of physiological heterogeneity. We show how noise levels and signal sensitivity can be set independently in molecular networks, but often changes in signal sensitivity affect noise propagation. Using theory and simulations, we show that negative feedback can both enhance and reduce noise. We identify a trade-off; noise reduction in one molecular intermediate by negative feedback is at the expense of increased noise in the levels of other molecules along the feedback loop. The reactants of the processes that are strongly (cooperatively) regulated, so as to allow for negative feedback with a high strength, will display enhanced noise