Genetic and biochemical differences in populations bred for extremes in maize grain methionine concentration

Background: Methionine is an important nutrient in animal feed and several approaches have been developed to increase methionine concentration in maize (Zea mays L.) grain. One approach is through traditional breeding using recurrent selection. Using divergent selection, genetically related populations with extreme differences in grain methionine content were produced. In order to better understand the molecular mechanisms controlling grain methionine content, we examined seed proteins, transcript levels of candidate genes, and genotypes of these populations. Results: Two populations were selected for high or low methionine concentration for eight generations and 40 and 56% differences between the high and low populations in grain methionine concentration were observed. Mean values between the high and low methionine populations differed by greater than 1.5 standard deviations in some cycles of selection. Other amino acids and total protein concentration exhibited much smaller changes. In an effort to understand the molecular mechanisms that contribute to these differences, we compared transcript levels of candidate genes encoding high methionine seed storage proteins involved in sulfur assimilation or methionine biosynthesis. In combination, we also explored the genetic mechanisms at the SNP level through implementation of an association analysis. Significant differences in methionine-rich seed storage protein genes were observed in comparisons of high and low methionine populations, while transcripts of seed storage proteins lacking high levels of methionine were unchanged. Seed storage protein levels were consistent with transcript levels. Two genes involved in sulfur assimilation, Cys2 and CgS1 showed substantial differences in allele frequencies when two selected populations were compared to the starting populations. Major genes identified across cycles of selection by a high-stringency association analysis included dzs18, wx, dzs10, and zp27. Conclusions: We hypothesize that transcriptional changes alter sink strength by altering the levels of methionine-rich seed storage proteins. To meet the altered need for sulfur, a cysteine-rich seed storage protein is altered while sulfur assimilation and methionine biosynthesis throughput is changed by selection for certain alleles of Cys2 and CgS1

Factors Influencing the Introduction of Value-Based Payment in Integrated Stroke Care: 'Evidence from a Qualitative Case Study'

Background: To address issues related to suboptimal insight in outcomes, fragmentation, and increasing costs, stakeholders are experimenting with value-based payment (VBP) models, aiming to facilitate high-value integrated care. However, insight in how, why and under what circumstances such models can be successful is limited. Drawing upon realist evaluation principles, this study identifies context factors and associated mechanisms influencing the introduction of VBP in stroke care. Methods: Existing knowledge on context-mechanism relations impacting the introduction of VBP programs (in real-world settings) was summarized from literature. These relations were then tested, refined, and expanded based on a case study comprising interviews with representatives from organizations involved in the introduction of a VBP model for integrated stroke care in Rotterdam, the Netherlands. Results: Facilitating factors were pre-existing trust-based relations, shared dissatisfaction with the status quo, regulatory compatibility and simplicity of the payment contract, gradual introduction of down-side risk for providers, and involvement of a trusted third party for data management. Yet to be addressed barriers included friction between short- and long-term goals within and among organizations, unwillingness to forgo professional and organizational autonomy, discontinuity in resources, and limited access to real-time data for improving care delivery processes. Conclusions: Successful payment and delivery system reform require long-term commitment from all stakeholders stretching beyond the mere introduction of new models. Careful consideration of creating the ‘right’ contextual circumstances remains crucially important, which includes willingness among all involved providers to bear shared financial and clinical responsibility for the entire care chain, regardless of where care is provided

Time Scale Hierarchies in the Functional Organization of Complex Behaviors

Traditional approaches to cognitive modelling generally portray cognitive events in terms of ‘discrete’ states (point attractor dynamics) rather than in terms of processes, thereby neglecting the time structure of cognition. In contrast, more recent approaches explicitly address this temporal dimension, but typically provide no entry points into cognitive categorization of events and experiences. With the aim to incorporate both these aspects, we propose a framework for functional architectures. Our approach is grounded in the notion that arbitrary complex (human) behaviour is decomposable into functional modes (elementary units), which we conceptualize as low-dimensional dynamical objects (structured flows on manifolds). The ensemble of modes at an agent’s disposal constitutes his/her functional repertoire. The modes may be subjected to additional dynamics (termed operational signals), in particular, instantaneous inputs, and a mechanism that sequentially selects a mode so that it temporarily dominates the functional dynamics. The inputs and selection mechanisms act on faster and slower time scales then that inherent to the modes, respectively. The dynamics across the three time scales are coupled via feedback, rendering the entire architecture autonomous. We illustrate the functional architecture in the context of serial behaviour, namely cursive handwriting. Subsequently, we investigate the possibility of recovering the contributions of functional modes and operational signals from the output, which appears to be possible only when examining the output phase flow (i.e., not from trajectories in phase space or time)