Anders Björklund

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.

Estimation of Wage Gains and Welfare Gains from Self-Selection Models

Abstract: In this paper we consider the basic self-selection model for the effects of education, training, unions, and other activities on wages. We show that past models have ignored "heterogeneity of rewards" to the activity--i.e., differences across individuals in the rate of return to the activity--as a source of selection bias. We model such heterogeneity, show how its presence can be tested, and draw out its implications for the wage and welfare gains to the activity. An empirical application provides strong support for such heterogeneity.

Neuroprotection in the rat Parkinson model by intrastriatal GDNF gene transfer using a lentiviral vector.

Abstract: We used a recombinantlentiviral vector (rLV) for gene delivery of GDNF to the striatum, and assessed its neuroprotective eiects in the intrastriatal 6 -hydroxydopamine (6 -OHDA) lesion model.The level of GDNF expression obtained with the rLV-GDNF vector was dose-related and ranged between 0.9^9.3 ng/mg tissue in the transduced striatum, as determined by ELISA, and 0.2^3.0 ng/mg tissue were detected in the ipsilateral substantia nigra (SN), due to anterograde transport of the GDNF protein. GDNF expression was apparentat4 days and maint ained for � 8 months after injection. Striatal delivery of rLV-GDNF e⁄ciently protected the nigral dopamine (DA) neurons and their projection, against the 6 -OHDA lesion (65^77% of intact side). Sprouting of the lesioned axons was observed along the nigrostriatal pathway, precisely corresponding to the areas containing anterogradely transported GDNF.NeuroReport13:75^ 82 � c 2002 Lippincott Williams & Wilkins.

Survival, growth and function of dopaminergic neurons grafted to the brain.

Abstract: Publisher Summary This chapter discusses recent advances made in the grafting of dopamine (DA) neurons with regard to factors that govern their survival, growth characteristics, functional capacity, and regulation by the host brain. It also highlights some of the problems facing possible future clinical application in this field. The rat DA system is eminently suitable for the study of neural grafts for several reasons. The normal anatomy of the rat DA system has been mapped in detail and there exist robust markers for the localization of DA neurons in the brain. In addition, the DA system is susceptible to relatively specific destruction using the neurotoxin, 6-hydroxydopamine, and, in combination with an extensive knowledge of DA pharmacology, it has been possible to create well-defined behavioral models in rats with damage to their DA system. These models have added interest in that the grafting of DA neurons inevitably bears some clinical relevance to Parkinson's disease, in which there is an extensive degeneration of nigrostriatal DA neurons.

Serotonin Neuron Loss and Nonmotor Symptoms Continue in Parkinson’s Patients Treated with Dopamine Grafts

Abstract: Cell therapy studies in patients with Parkinson's disease (PD) have been confined to intrastriatal transplantation of dopamine-rich fetal mesencephalic tissue in efforts to improve motor performance. Although some PD patients receiving the dopamine-rich grafts showed improvements in motor symptoms due to replacement of dopaminergic neurons, they still suffered from nonmotor symptoms including depression, fatigue, visual hallucinations, and sleep problems. Using functional imaging and clinical evaluation of motor and nonmotor symptoms in three PD patients transplanted with intrastriatal fetal grafts 13 to 16 years previously, we assessed whether reestablishment of dopaminergic neuronal networks is sufficient to improve a broad range of symptoms. At 13 to 16 years after transplantation, dopaminergic innervation was restored to normal levels in basal ganglia and preserved in a number of extrabasal ganglia areas. These changes were associated with long-lasting relief of motor symptoms. Then, we assessed the integrity of their serotonergic and norepinephrine neuronal systems using [¹¹C]DASB {[¹¹C]3-amino-4-(2-dimethylaminomethylphenylthio) benzonitrile} positron emission tomography (PET) and ¹⁸F-dopa PET, respectively. ¹⁸F-dopa uptake in the locus coeruleus was within the normal range. In contrast, [¹¹C]DASB uptake in the raphe nuclei and regions receiving serotonergic projections was markedly reduced. These results indicate ongoing degeneration of serotonergic raphe nuclei and their projections to regions involved in the regulation of sleep, arousal, feeding, satiety, mood, and emotion. Our findings indicate that future cell-based therapies using fetal tissue or stem cells in PD patients may require additional grafts of serotonergic neurons to relieve nonmotor symptoms by restoring serotonergic neurotransmission in specific cerebral targets.

Econometric Analysis of Cross Section and Panel Data

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.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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。

Econometric Analysis of Panel Data

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.