2,699 research outputs found
Prostaglandin F(2α) is required for NMDA receptor-mediated induction of c-fos mRNA in dentate gyrus neurons
No full textActivation of NMDA receptors has been linked to a diversity of lasting physiological and pathological changes in the mammalian nervous system. The cellular and molecular mechanisms underlying permanent modifications of nervous system structure and function after brief episodes of neuronal activity are unknown. Immediate-early genes (IEGs) have been implicated in the conversion of short-term stimuli to long-term changes in cellular phenotype by regulation of gene expression. The intracellular signaling pathways coupling activation of receptors at the cell surface with induction of lEGs in the nucleus are incompletely understood. NMDA produces a striking increase in the IEG c-fos in dentate gyrus (DG) neurons in vitro; this induction is dependent, in part, on the arachidonic acid cascade. Here we show that NMDA receptor activation triggers the synthesis of the prostaglandins PGF(2α) and PGE2, but not PGD2, in rat cerebral cortical neurons in vitro. We further demonstrate that PGF(2α), but not PGE2 or PGD2, is necessary but not sufficient for NMDA induction of c-fos mRNA in DG neurons. These findings provide insight into the molecular events coupling activation of the NMDA receptor with regulation of the IEG c-fos and identify the diffusable messenger PGF(2α) as obligatory for NMDA receptor-mediated transcription of a nuclear IEG
Author Attributions in Medieval Text Collections: An Exploration
No full textThis article examines the role and function of author attributions in multi-text manuscripts containing Dutch, English, French or German short verse narratives. The findings represent one strand of the investigations undertaken by the cross-European project ‘The Dynamics of the Medieval Manuscript’, which analysed the dissemination of short verse narratives and the principles of organisation underlying the compilation of text collections. Whilst short verse narratives are more commonly disseminated anonymously, there are manuscripts in which authorship is repeatedly attributed to a text or corpus. Through six case studies, this article explores medieval concepts of authorship and how they relate to constructions of authority, whether regarding an empirical figure or a literary construction. In addition, it looks at how authorship plays a role in manuscript compilation, and at the effects of attributions (by author and/or compiler) on reception. The case studies include manuscripts from the thirteenth to fifteenth centuries, produced in a range of social and cultural contexts, and featuring some of the most important European authors of short verse narratives: Rutebeuf, Baudouin de Condé, Der Striker, Konrad von Würzberg, Willem of Hildegaersberch, and Geoffrey Chaucer. The preliminary findings contribute to our understanding of author attributions in text collections from across northern Europe and point towards future lines of enquiry into the role of authorship in medieval textual dissemination
NMDA and non-NMDA receptor-mediated increase of c-fos mRNA in dentate gyrus neurons involves calcium influx via different routes
No full textWe examined the effects of selective agonists of ionotropic excitatory amino acid (EAA) receptor subtypes on induction of the immediate early gene c-fos. We used in situ hybridization to measure c-fos mRNA and fura-2 imaging to measure intracellular calcium (Ca2+i) in individual dentate gyrus neurons maintained in vitro. Activation of either NMDA or non-NMDA receptor subtypes is sufficient to induce the rapid and dramatic increase of c-fos mRNA. Activation of either NMDA or non-NMDA receptors also induces a rapid and dramatic increase of Ca2+i, effects blocked by the removal or chelation of extracellular calcium (Ca2+e). c- fos mRNA induction by either receptor subtype is Ca2+ dependent, since chelation of Ca2+e with EGTA prevents c-fos mRNA induction by both NMDA and non-NMDA receptor agonists. The increase in Ca2+i induced by activating non-NMDA receptors is inhibited either by removal of extracellular sodium (Na+e) or by the voltage-sensitive calcium channel (VSCC) blocker nifedipine. By contrast, the increase of Ca2+i induced by activating NMDA receptors is not inhibited by removal of Na+e or nifedipine. Consistent with these effects on Ca2+i, nifedipine inhibits induction of c-fos mRNA by non-NMDA, but not by NMDA, receptor agonists. These findings indicate that Ca2+ serves as a second messenger coupling ionotropic EAA receptors with transcriptional activation of c-fos mRNA. The route of Ca2+ entry into dentate neurons, however, depends on the EAA receptor subtype stimulated. Non-NMDA receptor activation results in Ca2+ influx indirectly via VSCCs, whereas NMDA receptor activation results in Ca2+ influx directly through the NMDA channel itself.(ABSTRACT TRUNCATED AT 250 WORDS)</jats:p
Batch Bayesian Learning of Large-Scale LS-SVMs Based on Low-rank Tensor Networks
No full textLeast Squares Support Vector Machines (LS-SVMs) are state-of-the-art learning algorithms that have been widely used for pattern recognition. The solution for an LS-SVM is found by solving a system of linear equations, which involves the computational complexity of O(N^3). When datasets get larger, solving LS-SVM problems with standard methods becomes burdensome or even unfeasible. The Tensor Train (TT) decomposition provides an approach to representing data in highly compressed formats without loss of accuracy. By converting vectors and matrices in the TT format, the storage and computational requirements can be greatly reduced. In this thesis, we develop a Bayesian learning method in the TT format to solve large-scale LS-SVM problems, which involves the computation of a matrix inverse. This method allows us to include the information we know about the model parameters in the prior distribution. As a result, we are able to obtain a probability distribution of the parameters, which enables us to construct confidence levels of the predictions. In the numerical experiment, we show that the developed method performs competitively with the current methods.Mechanical Engineering | Systems and Contro
Additive Manufacturing: Polymers Applicable for Laser Sintering (LS)
No full textAbstractAdditive Manufacturing (AM) is close to become a production technique changing the way of part fabrication in future. Enhanced complexity and personalized features are aimed. The expectations in AM for the future are enormous and betimes it is considered as kind of the next industrial revolution. Laser Sintering (LS) of polymer powders is one component of the AM production techniques. However materials successfully applicable to Laser Sintering (LS) are very limited today. The presentation picks up this topic and gives a short introduction on the material available today. Important factors of polymer powders, their significance for effective LS processing and analytical approaches to access those values are presented in the main part. Concurrently the exceptional position of polyamide 12 powders is this connection is outlined
The Social Cost-of-Living: Welfare Foundations and Estimation
No full textWe present a new class of social cost-of-living indices and a nonparametric framework for estimating these and other social cost-of- living indices. Common social cost-of-living indices can be understood as aggregator functions of approximations of individual cost-of-living indices. The Consumer Price Index (CPI) is the expenditure-weighted average of first-order approximations of each individual’s cost-of-living index. This is troubling for three reasons. First, it has not been shown to have a welfare economic foundation for the case where agents are heterogeneous (as they clearly are.) Second, it uses an expenditure-weighted average which downweights the experience of poor households relative to rich households. Finally, it uses only first-order approximations of each individual’s cost-of-living index, and thus ignores substitution effects. We propose a “common-scaling” social cost-of-living index, which is defined as the single scaling to everyone’s expenditure which holds social welfare constant across a price change. Our approach has an explicit social welfare foundation and allows us to choose the weights on the costs of rich and poor households. We also give a unique solution for the welfare function for the case where the weights are independent of household expenditure. A first order approximation of our social cost-of- living index nests as special cases commonly used indices such as the CPI. We also provide a nonparametric method for estimating second- order approximations (which account for substitution effects).Inflation, Social cost-of-living, Demand, Average Derivatives
The Social Cost-of-Living: Welfare Foundations and Estimation
No full textWe present a new class of social cost-of-living indices and a nonparametric framework for estimating these and other social cost-of-living indices. Common social cost-of-living indices can be understood as aggregator functions of approximations of individual cost-of-living indices. The Consumer Price Index (CPI) is the expenditure-weighted average of first-order approximations of each individual’s cost-of-living index. This is troubling for three reasons. First, it has not been shown to have a welfare economic foundation for the case where agents are heterogeneous (as they clearly are.) Second, it uses an expenditure-weighted average which downweights the experience of poor households relative to rich households. Finally, it uses only first-order approximations of each individual’s cost-of-living index, and thus ignores substitution effects. We propose a “common-scaling” social cost-of-living index, which is defined as the single scaling to everyone’s expenditure which holds social welfare constant across a price change. Our approach has an explicit social welfare foundation and allows us to choose the weights on the costs of rich and poor households. We also give a unique solution for the welfare function for the case where the weights are independent of household expenditure. A first order approximation of our social cost-of-living index nests as special cases commonly used indices such as the CPI. We also provide a nonparametric method for estimating second-order approximations (which account for substitution effects).Inflation, Social cost-of-living, Demand, Average derivatives
Tell us our story: Understanding 'religion and violence' in multiple contexts of learning
No full textThis article raises the question about how definitions of religion and violence can be understood as links to the context in which they are formulated. The focus is on the context of academic learning. Understanding a definition as a micro-narrative that reflects the cultural 'archive', the author uses two academic contexts (i.e. Utrecht, The Netherlands and Jakarta, Indonesia) to show how religion and violence are differently understood. These differences are taken as significant information for understanding how the topic of 'religion and violence' is related to cultural understandings of the place of religion in society. The question is raised how 'narrative learning' can help as a strategy to raise awareness about the preconditioning of (academic) definitions of 'religion and violence'
Epileptic Seizure Detection using a Tensor-Network Kalman Filter for LS-SVMs
No full textEpilepsy is one of the most common neurological conditions, affecting nearly 1% of the global population. It is defined by the seemingly random occurrence of spontaneous seizures. Anti-epileptic drugs provide adequate treatment for about 70% of patients. The remaining 30%, on the other hand, continue to have seizures, which has a significant impact on their quality of life as they are constantly unsure when these seizures will occur. Reliable seizure detection methods would thus have a significant impact on the lives of these patients. Despite ongoing research efforts involving academia and industry in large international collaborations, epileptic seizure detection and especially prediction is still an unsolved problem. The key to the solution could lie within ultralong-term, reallife datasets that are currently being generated using wearable sensors. However, due to the size of these datasets, conventional learning techniques such as least-square support vector machines (LS-SVMs) can become intractable. Therefore, this work proposes the use of a recently developed tensor network Kalman filtering approach for LS-SVMs (TNKFLSSVM) to detect epileptic seizures [1]. In the TNKF-LSSVM algorithm, the dual problem of the LS-SVM is solved using a recursive Bayesian filtering approach. This way the least-square problem can be solved row-by-row using a Kalman filter, thereby avoiding explicit matrix inversions, while also being able to provide confidence bounds on the estimates. By making use of the tensor-train format [2] to represent the matrices and vectors in the Kalman equations, it is even possible to avoid the construction of the (N + 1) × (N + 1) covariance matrix1. To be able to apply the TNKF-LSSVM algorithm for seizure detection there are still some issues that need to be tackled. One such problem is that the TNKF-LSSVM only performs well when the dataset is properly balanced, which is generally not the case for seizure datasets. Furthermore, for the TNKF-LSSVM to work efficiently for large scale problems the modes of the tensortrains representing the matrices and vectors should be as small as possible, thus it must hold that N + 1 = Q i ni, such that ni is ‘small’ for all i. To overcome both of these challenges we propose using the SMOTE method to oversample the seizure class, such that a balanced training set can be generated that has good factorization properties. Some preliminary results using a small subset of data from a public EEG dataset [3] show that taking the above considerations into account, the TNKF-LSSVM method can have performance that is competitive with a regular LS-SVM. Where the TNKFLSSVM method has the benefit of scaling log-linearly with the size of the dataset (in terms of memory usage) and can provide an uncertainty estimate of the detection. Future work will need 1N is the number of data points in the training set and 1 is added for the bias. to show whether this scaling up works as expected for the entire dataset.Signal Processing System
LS-RAPID Manual with Video Tutorials
No full textLS-RAPID is an integrated simulation model capable of capturing the entire landslide process starting from a state of stability to landslide initiation and movement to the mass deposition. This paper provides an overview of the use of LS-RAPID to simulate landslide case histories around the world, provides a manual for readers to begin using the program for simulations, and describes the use of the program for several models. Specific steps to use the basic and advanced features in LS-RAPID are provided within the paper. Additionally, video tutorials are provided to supplement the descriptive steps provided in this paper. These tutorials are developed to focus on individual aspects of the program. The paper concludes with three tutorials that provide a complete walk-through of the use of the program from start to finish. These tutorials are for (1) an example of a rainfall-induced failure, (2) an example of an earthquake-induced failure, and (3) the case study of the Atami debris flow. The Atami debris flow illustrates the ability of LS-RAPID to reproduce reliable results associated with the initiation and runout motions of the observed failure.This book chapter is published as Ajmera, B., Ahari, H.E., Loi, D.H., Setiawan, H., Dang, K., Sassa, K. (2023). LS-RAPID Manual with Video Tutorials. In: Sassa, K., Konagai, K., Tiwari, B., Arbanas, Ž., Sassa, S. (eds) Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022. Progress in Landslide Research and Technology. Springer, Cham.
DOI: 10.1007/978-3-031-16898-7_26.
Copyright 2023 The Author(s).
Attribution 4.0 International (CC BY 4.0).
Posted with permission
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