Author
Anders Björklund
Other affiliations: University of Washington, Institute for the Study of Labor, University of Gothenburg ...read more
Bio: Anders Björklund is an academic researcher from Lund University. The author has contributed to research in topics: Transplantation & Dopamine. The author has an hindex of 165, co-authored 769 publications receiving 84268 citations. Previous affiliations of Anders Björklund include University of Washington & Institute for the Study of Labor.
Topics: Transplantation, Dopamine, Striatum, Dopaminergic, Substantia nigra
Papers published on a yearly basis
Papers
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TL;DR: The results indicate that the serotoninergic neuron system has a facilitatory role in the regulation of pituitary LH secretion in the male rat and lend support to the idea that the recovery of the neuroendocrine regulatory mechanisms is caused by an actual regeneration of the drug-lesioned serotonin axons.
71 citations
TL;DR: The results support the view that the basal forebrain cholinergic system may be implicated in the acquisition rather than retention of spatial memory in the water maze task and suggest that preoperative training on the task may reduce the functional consequences of a subsequent cholinergic lesion.
71 citations
TL;DR: The results confirm that the adrenergic nerves in the human uterus, like those in uterine horns of laboratory animals, undergo fundamental changes in the course of pregnancy.
71 citations
TL;DR: This chapter describes the current conditions for the acquisition and preparation of human fetal tissue for intracerebral grafting and to discuss possible optimization and perspectives for its future use.
Abstract: Publisher Summary This chapter describes the current conditions for the acquisition and preparation of human fetal tissue for intracerebral grafting and to discuss possible optimization and perspectives for its future use. The chapter focuses on the transplantation of dopamine (DA) neurons in experimental and clinical Parkinsonism. The use of human fetal brain tissue for transplantation purposes inevitably brings up several basic medical ethical issues. Following the routine suction abortion procedure, it is possible to identify and dissect a ventral mesencephalo (VM) piece in 50–75% of cases. The search for fetal tissue and dissection of CNS is conducted in a sterile hood by an investigator dressed in full sterile gear. The aborted material, which includes other parts of uterine content in addition to the fetus, is transported from the operating theatre to the place of dissection in a sterile Petri dish. The material is inspected initially to find pieces of the fetus, without a microscope, typically by utilizing a large pair of forceps and by rinsing away blood using a syringe containing sterile saline. Xenografting experiments to rats have revealed some inherent properties of the human fetal neural cells that are useful to bear in mind also when considering the clinical scenario.
71 citations
TL;DR: It is concluded that c‐Jun induction and phosphorylation may be involved in the cellular events leading to death of nigral dopaminergic neurons in vivo and that this response can be modulated by glial‐cell‐line‐derived‐neurotrophic factor.
Abstract: This study was designed to determine whether induction and phosphorylation of the transcription factor c-Jun is associated with lesion-induced death of dopaminergic neurons of the substantia nigra pars compacta, and if this cellular response is modulated by glial-cell-line-derived neurotrophic factor. In adult rats, delayed dopaminergic neuron cell death induced by intrastriatal 6-hydroxydopamine injection led to a marked increase in the number of both c-Jun- and phosphorylated c-Jun-immunoreactive nuclei in the substantia nigra pars compacta. The response was maximal before any significant loss of nigral neurons could be detected (on day 7 post lesion) and was confined to the dopaminergic neurons. Similarly, 6-hydroxydopamine lesion of the striatal dopaminergic terminals or excitotoxic lesion of the striatal target neurons in neonatal rats resulted in an increased number of c-Jun- and phosphorylated c-Jun-immunoreactive nigral nuclei that preceded the loss of nigral dopaminergic neurons. By contrast, after an excitotoxic lesion of the striatal target neurons in the adult rat, resulting in atrophy but not cell death of the nigral dopaminergic neurons, no upregulation of either c-Jun or phosphorylated c-Jun was found. A single injection of 10 microg of glial-cell-line-derived-neurotrophic factor given at day 3 after the intrastriatal 6-hydroxydopamine lesion reduced the number of c-Jun- and phosphorylated c-Jun-immunoreactive nuclei in the substantia nigra and protected the dopaminergic neurons from the ensuing cell death. We conclude that c-Jun induction and phosphorylation may be involved in the cellular events leading to death of nigral dopaminergic neurons in vivo and that this response can be modulated by glial-cell-line-derived-neurotrophic factor.
71 citations
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Book•
01 Jan 2001
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Abstract: The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
28,298 citations
28 Jul 2005
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
Book•
28 Apr 2021
TL;DR: In this article, the authors proposed a two-way error component regression model for estimating the likelihood of a particular item in a set of data points in a single-dimensional graph.
Abstract: Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.
10,363 citations
9,362 citations
TL;DR: PD models based on the manipulation of PD genes should prove valuable in elucidating important aspects of the disease, such as selective vulnerability of substantia nigra dopaminergic neurons to the degenerative process.
4,872 citations