148 research outputs found

    Economic-demographic interactions in long-run growth

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    Cliometrics confirms that Malthus’ model of the pre-industrial economy, in which increases in productivity raise population but higher population drives down wages, is a good description for much of demographic/economic history. A contributor to the Malthusian equilibrium was the Western European Marriage Pattern, the late age of female first marriage, which promised to retard the fall of living standards by restricting fertility. The demographic transition and the transition from Malthusian economies to modern economic growth attracted many Cliometric models surveyed here. A popular model component is that lower levels of mortality over many centuries increased the returns to, or preference for, human capital investment so that technical progress eventually accelerated. This initially boosted birth rates and population growth accelerated. Fertility decline was earliest and most striking in late eighteenth century France. By the 1830s the fall in French marital fertility is consistent with a response to the rising opportunity cost of children. The rest of Europe did not begin to follow until end of the nineteenth century. Interactions between the economy and migration have been modelled with Cliometric structures closely related to those of natural increase and the economy. Wages were driven up by emigration from Europe and reduced in the economies receiving immigrants

    Perception of Loudness Is Influenced by Emotion

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    Loudness perception is thought to be a modular system that is unaffected by other brain systems. We tested the hypothesis that loudness perception can be influenced by negative affect using a conditioning paradigm, where some auditory stimuli were paired with aversive experiences while others were not. We found that the same auditory stimulus was reported as being louder, more negative and fear-inducing when it was conditioned with an aversive experience, compared to when it was used as a control stimulus. This result provides support for an important role of emotion in auditory perception

    A four phase development model for integrated care services in the Netherlands

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    Background. Multidisciplinary and interorganizational arrangements for the delivery of coherent integrated care are being developed in a large number of countries. Although there are many integrated care programs worldwide, the process of developing these programs and interorganizational collaboration is described in the literature only to a limited extent. The purpose of this study is to explore how local integrated care services are developed in the Netherlands, and to conceptualize and operationalize a development model of integrated care. Methods. The research is based on an expert panel study followed by a two-part questionnaire, designed to identify the development process of integrated care. Essential elements of integrated care, which were developed in a previous Delphi and Concept Mapping Study, were analyzed in relation to development process of integrated care. Results. Integrated care development can be characterized by four developmental phases: the initiative and design phase; the experimental and execution phase; the expansion and monitoring phase; and the consolidation and transformation phase. Different elements of integrated care have been identified in the various developmental phases. Conclusion. The findings provide a descriptive model of the development process that integrated care services can undergo in the Netherlands. The findings have important implications for integrated care services, which can use the model as an instrument to reflect on their current practices. The model can be used to help to identify improvement areas in practice. The model provides a framework for developing evaluation designs for integrated care arrangements. Further research is recommended to test the developed model in practice and to add international experiences

    The Golden Beauty: Brain Response to Classical and Renaissance Sculptures

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    Is there an objective, biological basis for the experience of beauty in art? Or is aesthetic experience entirely subjective? Using fMRI technique, we addressed this question by presenting viewers, naïve to art criticism, with images of masterpieces of Classical and Renaissance sculpture. Employing proportion as the independent variable, we produced two sets of stimuli: one composed of images of original sculptures; the other of a modified version of the same images. The stimuli were presented in three conditions: observation, aesthetic judgment, and proportion judgment. In the observation condition, the viewers were required to observe the images with the same mind-set as if they were in a museum. In the other two conditions they were required to give an aesthetic or proportion judgment on the same images. Two types of analyses were carried out: one which contrasted brain response to the canonical and the modified sculptures, and one which contrasted beautiful vs. ugly sculptures as judged by each volunteer. The most striking result was that the observation of original sculptures, relative to the modified ones, produced activation of the right insula as well as of some lateral and medial cortical areas (lateral occipital gyrus, precuneus and prefrontal areas). The activation of the insula was particularly strong during the observation condition. Most interestingly, when volunteers were required to give an overt aesthetic judgment, the images judged as beautiful selectively activated the right amygdala, relative to those judged as ugly. We conclude that, in observers naïve to art criticism, the sense of beauty is mediated by two non-mutually exclusive processes: one based on a joint activation of sets of cortical neurons, triggered by parameters intrinsic to the stimuli, and the insula (objective beauty); the other based on the activation of the amygdala, driven by one's own emotional experiences (subjective beauty)

    Ubiquitous LEA29Y Expression Blocks T Cell Co-Stimulation but Permits Sexual Reproduction in Genetically Modified Pigs

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    We have successfully established and characterized a genetically modified pig line with ubiquitous expression of LEA29Y, a human CTLA4-Ig derivate. LEA29Y binds human B7.1/CD80 and B7.2/CD86 with high affinity and is thus a potent inhibitor of T cell co-stimulation via this pathway. We have characterized the expression pattern and the biological function of the transgene as well as its impact on the porcine immune system and have evaluated the potential of these transgenic pigs to propagate via assisted breeding methods. The analysis of LEA29Y expression in serum and multiple organs of CAG-LEA transgenic pigs revealed that these animals produce a biologically active transgenic product at a considerable level. They present with an immune system affected by transgene expression, but can be maintained until sexual maturity and propagated by assisted reproduction techniques. Based on previous experience with pancreatic islets expressing LEA29Y, tissues from CAG-LEA29Y transgenic pigs should be protected against rejection by human T cells. Furthermore, their immune-compromised phenotype makes CAG-LEA29Y transgenic pigs an interesting large animal model for testing human cell therapies and will provide an important tool for further clarifying the LEA29Y mode of action

    Robust simplifications of multiscale biochemical networks

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    <p>Abstract</p> <p>Background</p> <p>Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.</p> <p>Results</p> <p>We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-<it>κ</it>B pathway.</p> <p>Conclusion</p> <p>Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.</p

    OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities

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    Microorganisms rarely live isolated in their natural environments but rather function in consolidated and socializing communities. Despite the growing availability of high-throughput sequencing and metagenomic data, we still know very little about the metabolic contributions of individual microbial players within an ecological niche and the extent and directionality of interactions among them. This calls for development of efficient modeling frameworks to shed light on less understood aspects of metabolism in microbial communities. Here, we introduce OptCom, a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level and multi-objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. In contrast to earlier approaches that rely on a single objective function, here, we consider species-level fitness criteria for the inner problems while relying on community-level objective maximization for the outer problem. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species (or guilds) involved. We applied OptCom to quantify the syntrophic association in a well-characterized two-species microbial system, assess the level of sub-optimal growth in phototrophic microbial mats, and elucidate the extent and direction of inter-species metabolite and electron transfer in a model microbial community. We also used OptCom to examine addition of a new member to an existing community. Our study demonstrates the importance of trade-offs between species- and community-level fitness driving forces and lays the foundation for metabolic-driven analysis of various types of interactions in multi-species microbial systems using genome-scale metabolic models

    A Biological Global Positioning System: Considerations for Tracking Stem Cell Behaviors in the Whole Body

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    Many recent research studies have proposed stem cell therapy as a treatment for cancer, spinal cord injuries, brain damage, cardiovascular disease, and other conditions. Some of these experimental therapies have been tested in small animals and, in rare cases, in humans. Medical researchers anticipate extensive clinical applications of stem cell therapy in the future. The lack of basic knowledge concerning basic stem cell biology-survival, migration, differentiation, integration in a real time manner when transplanted into damaged CNS remains an absolute bottleneck for attempt to design stem cell therapies for CNS diseases. A major challenge to the development of clinical applied stem cell therapy in medical practice remains the lack of efficient stem cell tracking methods. As a result, the fate of the vast majority of stem cells transplanted in the human central nervous system (CNS), particularly in the detrimental effects, remains unknown. The paucity of knowledge concerning basic stem cell biology—survival, migration, differentiation, integration in real-time when transplanted into damaged CNS remains a bottleneck in the attempt to design stem cell therapies for CNS diseases. Even though excellent histological techniques remain as the gold standard, no good in vivo techniques are currently available to assess the transplanted graft for migration, differentiation, or survival. To address these issues, herein we propose strategies to investigate the lineage fate determination of derived human embryonic stem cells (hESC) transplanted in vivo into the CNS. Here, we describe a comprehensive biological Global Positioning System (bGPS) to track transplanted stem cells. But, first, we review, four currently used standard methods for tracking stem cells in vivo: magnetic resonance imaging (MRI), bioluminescence imaging (BLI), positron emission tomography (PET) imaging and fluorescence imaging (FLI) with quantum dots. We summarize these modalities and propose criteria that can be employed to rank the practical usefulness for specific applications. Based on the results of this review, we argue that additional qualities are still needed to advance these modalities toward clinical applications. We then discuss an ideal procedure for labeling and tracking stem cells in vivo, finally, we present a novel imaging system based on our experiments
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