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To cope with the already large, and ever increasing, amount of information stored in organizational memory, "forgetting," as an important human memory process, might be transferred to the organizational context. Especially in intentionally planned change processes (e.g., change management), forgetting is an important precondition to impede the recall of obsolete routines and adapt to new strategic objectives accompanied by new organizational routines. We first comprehensively review the literature on the need for organizational forgetting and particularly on accidental vs. intentional forgetting. We discuss the current state of the art of theory and empirical evidence on forgetting from cognitive psychology in order to infer mechanisms applicable to the organizational context. In this respect, we emphasize retrieval theories and the relevance of retrieval cues important for forgetting. Subsequently, we transfer the empirical evidence that the elimination of retrieval cues leads to faster forgetting to the forgetting of organizational routines, as routines are part of organizational memory. We then propose a classification of cues (context, sensory, business process-related cues) that are relevant in the forgetting of routines, and discuss a meta-cue called the "situational strength" cue, which is relevant if cues of an old and a new routine are present simultaneously. Based on the classification as business process-related cues (information, team, task, object cues), we propose mechanisms to accelerate forgetting by eliminating specific cues based on the empirical and theoretical state of the art. We conclude that in intentional organizational change processes, the elimination of cues to accelerate forgetting should be used in change management practices.
Two decades ago, sphingosine 1-phosphate (S1P) was discovered as a novel bioactive molecule that regulates a variety of cellular functions. The plethora of S1P-mediated effects is due to the fact that the sphingolipid not only modulates intracellular functions but also acts as a ligand of G protein-coupled receptors after secretion into the extracellular environment. In the plasma, S1P is found in high concentrations, modulating immune cell trafficking and vascular endothelial integrity. The liver is engaged in modulating the plasma S1P content, as it produces apolipoprotein M, which is a chaperone for the S1P transport. Moreover, the liver plays a substantial role in glucose and lipid homeostasis. A dysfunction of glucose and lipid metabolism is connected with the development of liver diseases such as hepatic insulin resistance, non-alcoholic fatty liver disease, or liver fibrosis. Recent studies indicate that S1P is involved in liver pathophysiology and contributes to the development of liver diseases. In this review, the current state of knowledge about S1P and its signaling in the liver is summarized with a specific focus on the dysregulation of S1P signaling in obesity-mediated liver diseases. Thus, the modulation of S1P signaling can be considered as a potential therapeutic target for the treatment of hepatic diseases.
Moving Beyond ERP Components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
The article is a review of Patrick Gray's latest monograph: Shakespeare and the Fall of the Roman Republic: Selfhood, Stoicism and Civil War. Gray analyzes Shakespare's and his characters' representation of the 'self' in Julius Caesar and Antony and Cleopatra, with Coriolanus used for comparative purposes. The book induced a lively discussion of its content in academic community.
Introduction:
We aim to highlight the utility of this model in the analysis of the psycho-behavioral implications of family cancer, presenting the scientific literature that used Leventhal’s model as the theoretical framework of approach.
Material and methods:
A systematic search was performed in six databases (EBSCO, ScienceDirect, PubMed Central, ProQuest, Scopus, and Web of Science) with empirical studies published between 2006 and 2015 in English with regard to the Common Sense Model of Self-Regulation (CSMR) and familial/hereditary cancer. The key words used were: illness representations, common sense model, self regulatory model, familial/hereditary/genetic cancer, genetic cancer counseling. The selection of studies followed the PRISMA-P guidelines (Moher et al., 2009; Shamseer et al., 2015), which suggest a three-stage procedure.
Results:
Individuals create their own cognitive and emotional representation of the disease when their health is threatened, being influenced by the presence of a family history of cancer, causing them to adopt or not a salutogenetic behavior. Disease representations, particularly the cognitive ones, can be predictors of responses to health threats that determine different health behaviors. Age, family history of cancer, and worrying about the disease are factors associated with undergoing screening. No consensus has been reached as to which factors act as predictors of compliance with cancer screening programs.
Conclusions:
This model can generate interventions that are conceptually clear as well as useful in regulating the individuals’ behaviors by reducing the risk of developing the disease and by managing as favorably as possible health and/or disease.
As a tumor suppressor and the most frequently mutated gene in cancer, p53 is among the best-described molecules in medical research. As cancer is in most cases an age-related disease, it seems paradoxical that p53 is so strongly conserved from early multicellular organisms to humans. A function not directly related to tumor suppression, such as the regulation of metabolism in nontransformed cells, could explain this selective pressure. While this role of p53 in cellular metabolism is gradually emerging, it is imperative to dissect the tissue-and cell-specific actions of p53 and its downstream signaling pathways. In this review, we focus on studies reporting p53's impact on adipocyte development, function, and maintenance, as well as the causes and consequences of altered p53 levels in white and brown adipose tissue (AT) with respect to systemic energy homeostasis. While whole body p53 knockout mice gain less weight and fat mass under a high-fat diet owing to increased energy expenditure, modifying p53 expression specifically in adipocytes yields more refined insights: (1) p53 is a negative regulator of in vitro adipogenesis; (2) p53 levels in white AT are increased in diet-induced and genetic obesity mouse models and in obese humans; (3) functionally, elevated p53 in white AT increases senescence and chronic inflammation, aggravating systemic insulin resistance; (4) p53 is not required for normal development of brown AT; and (5) when p53 is activated in brown AT in mice fed a high-fat diet, it increases brown AT temperature and brown AT marker gene expression, thereby contributing to reduced fat mass accumulation. In addition, p53 is increasingly being recognized as crucial player in nutrient sensing pathways. Hence, despite existence of contradictory findings and a varying density of evidence, several functions of p53 in adipocytes and ATs have been emerging, positioning p53 as an essential regulatory hub in ATs. Future studies need to make use of more sophisticated in vivo model systems and should identify an AT-specific set of p53 target genes and downstream pathways upon different (nutrient) challenges to identify novel therapeutic targets to curb metabolic diseases
Evaluating climate geoengineering proposals in the context of the Paris Agreement temperature goals
(2018)
Current mitigation efforts and existing future commitments are inadequate to accomplish the Paris Agreement temperature goals. In light of this, research and debate are intensifying on the possibilities of additionally employing proposed climate geoengineering technologies, either through atmospheric carbon dioxide removal or farther-reaching interventions altering the Earth’s radiative energy budget. Although research indicates that several techniques may eventually have the physical potential to contribute to limiting climate change, all are in early stages of development, involve substantial uncertainties and risks, and raise ethical and governance dilemmas. Based on present knowledge, climate geoengineering techniques cannot be relied on to significantly contribute to meeting the Paris Agreement temperature goals.
Reviews and syntheses
(2018)
The cycling of carbon (C) between the Earth surface and the atmosphere is controlled by biological and abiotic processes that regulate C storage in biogeochemical compartments and release to the atmosphere. This partitioning is quantified using various forms of C-use efficiency (CUE) - the ratio of C remaining in a system to C entering that system. Biological CUE is the fraction of C taken up allocated to biosynthesis. In soils and sediments, C storage depends also on abiotic processes, so the term C-storage efficiency (CSE) can be used. Here we first review and reconcile CUE and CSE definitions proposed for autotrophic and heterotrophic organisms and communities, food webs, whole ecosystems and watersheds, and soils and sediments using a common mathematical framework. Second, we identify general CUE patterns; for example, the actual CUE increases with improving growth conditions, and apparent CUE decreases with increasing turnover. We then synthesize > 5000CUE estimates showing that CUE decreases with increasing biological and ecological organization - from uni-cellular to multicellular organisms and from individuals to ecosystems. We conclude that CUE is an emergent property of coupled biological-abiotic systems, and it should be regarded as a flexible and scale-dependent index of the capacity of a given system to effectively retain C.
Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.