Estimating and forecasting with a dynamic spatial panel data model

This paper focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006), a dynamic spatial GMM estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the Spatial AutoRegressive (SAR) error model. The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non-spatial estimators and illustrate our approach with an application to new economic geography

AGE-RELATED MACULAR DEGENERATION (AMD): FROM METABOLOMICS APPROACH TO THE INHIBITION OF PDK AS A NEW THERAPEUTIC TARGET

Age-related Macular Degeneration (AMD) is a leading cause of vision loss in the western world among people aged 50 or older. 90% of all vision loss due to AMD results from the exudative form, which is characterized by choroidal neovascularization (CNV). Age-related changes that induce pathologic CNV are incompletely understood. A successful application of anti-VEGF approaches in the clinic is obviously a turning point in AMD treatment. Nevertheless, despite such important advances, critical issues remain to be addressed. To better understand the aetiology of this pathology, we used and improved a murine model of laser-induced choroidal neovascularization and applied a 1H NMR metabolomics study. This approach leads to the emergence of different putative biomarkers and to the validation of the CNV model for an experimental study of AMD. Among these “biomarkers”, lactate appears to be clearly involved in the development of AMD. The modulation of their plasma concentration by treatment of the animals with synthetic compounds and more specifically Pyruvate Dehydrogenase Kinase inhibitors (PDK) significantly decrease the impact of laser induced CNV. Starting from these results, the development of new PDHK inhibitors could open the way to innovative treatment opportunities in AMD diseas

Insight into SUCNR1 (GPR91) structure and function

SUCNR1 (or GPR91) belongs to the family of G protein-coupled receptors (GPCR), which represents the largest group of membrane proteins in human genome. The majority of marketed drugs targets GPCRs, directly or indirectly. SUCNR1 has been classified as an orphan receptor until a landmark study paired it with succinate, a citric acid cycle intermediate. According to the current paradigm, succinate triggers SUCNR1 signaling pathways to indicate local stress that may affect cellular metabolism. SUCNR1 implication has been well documented in renin-induced hypertension, ischemia/reperfusion injury, inflammation and immune response, platelet aggregation and retinal angiogenesis. In addition, the SUCNR1-induced increase of blood pressure may contribute to diabetic nephropathy or cardiac hypertrophy. The understanding of SUCNR1 activation, signaling pathways and functions remains largely elusive, which calls for deeper investigations. SUCNR1 shows a high potential as an innovative drug target and is probably an important regulator of basic physiology. In order to achieve the full characterization of this receptor,more specific pharmacological tools such as small-molecules modulators will represent an important asset. In this review, we describe the structural features of SUCNR1, its current ligands and putative binding pocket. We give an exhaustive overview of the known and hypothetical signaling partners of the receptor in different in vitro and in vivo systems. The link between SUCNR1 intracellular pathways and its pathophysiological roles are also extensively discussed

Neuregulin-1 modulates the differentiation of neural stem cells in vitro trough an interaction with the Swi/Snf complex.

The neuregulin-1 (Nrg-1) gene is translated into several protein isoforms, which are either secreted or membrane-anchored. In vitro, neural stem cells (NSC) express mainly the cystein-rich-domain NRG (CRD-NRG) isoform, a membrane-anchored type III form. This isoform exhibits a cystein-rich-domain, which constitutes a second transmembrane domain and can be cleaved to release both a signaling EGF-containing domain (ECD) at the cell surface and an intracellular domain (ICD). The main goal of this paper was to determine the exact role of ECD and ICD in NSC survival and differentiation. Using an siRNA approach, we demonstrated that CRD-NRG inhibition was followed by a decrease in NSC proliferation and of neuronal or oligodendroglial differentiation. Overexpression of ICD but not ECD was followed by a decrease in NSC proliferation and an increase in neuronal and oligodendroglial differentiation. Moreover, we showed that ICD physically interacted in cultured NSC with BRM and BAF57, two members of the Swi/Snf remodeling complex, and that ICD stimulation of neuronal cell differentiation is dependent on the presence of BAF57

Nouveaux agents anti-inflammatoires en série N-(4-pyridyl)alcanesulfonamide

peer reviewedaudience: researcher, professional, studen

Spatial Lag Models with Nested Random Effects: An Instrumental Variable Procedure with an Application to English House Prices

This paper sets up a nested random effects spatial autoregressive panel data model to explain annual house price variation for 2000-2007 across 353 local authority districts in England. The estimation problem posed is how to allow for the endogeneity of the spatial lag variable producing the simultaneous spatial spillover of prices across districts together with the nested random effects in a panel data setting. To achieve this, the paper proposes new estimators based on the instrumental variable approaches of Kelejian and Prucha (1998) and Lee (2003) for the cross-sectional spatial autoregressive model. Monte Carlo results show that our estimators perform well relative to alternative approaches and produces estimates based on real data that are consistent with the theoretical house price model underpinning the reduced form

A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors

This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic panel data model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi, Fingleton and Pirotte (2014) and Fingleton (2008). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run effects and evaluate the predictive efficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001-2012, with the last two years reserved for prediction

N-Aryl-N'-(chroman-4-yl)ureas and thioureas display in vitro anticancer activity and selectivity on apoptosis-resistant glioblastoma cells: screening, synthesis of simplified derivatives, and structure-activity relationship analysis.

A series of chroman derivatives previously reported as potassium channel openers, as well as some newly synthesized simplified structures, were examined for their in vitro effects on the growth of three human high-grade glioma cell lines: U373, T98G, and Hs683. Significant in vitro growth inhibitory activity was observed with 2,2-dimethylchroman-type nitro-substituted phenylthioureas, such as compounds 4o and 4p. Interestingly, most tested phenylureas were found to be slightly less active, but more cell selective (normal versus tumor glial cells, such as 3d, 3e, and 3g), thus less toxic, than the corresponding phenylthioureas. No significant differences were observed in terms of chroman-derivative-induced growth inhibitory effects between glioma cells sensitive to pro-apoptotic stimuli (Hs683 glioma cells) and glioma cells associated with various levels of resistance to pro-apoptotic stimuli (U373 and T98G glioma cells), a feature that suggests non-apoptotic-mediated growth inhibition. Flow cytometry analyses confirmed the absence of pro-apoptotic effects for phenylthioureas and phenylureas when analyzed in U373 glioma cells and demonstrated U373 cell cycle arrest in the G0/G1 phase. Computer-assisted phase-contrast videomicroscopy revealed that 3d and 3g displayed cytostatic effects, while 3e displayed cytotoxic ones. As a result, this work identified phenylurea-type 2,2-dimethylchromans as a new class of antitumor agents to be further explored for an innovative therapeutic approach for high-grade glioma and/or for a possible new mechanism of action