An empirical investigation of the portuguese housing prices: evidence from the period 2004-2018

Housing market is an essential segment of the whole country’s economy. Recently, the rising house prices in Portugal reflects a booming economy. This article presents an integrated macro view of the Portuguese housing market with macroeconomic indicators. Firstly, it compares the housing market and several macroeconomic indicators from 2004 to 2018. Then, the dynamic analysis of the housing prices by different regions in Portugal and its typology included. Also, the article is complemented with the regression analysis to identify the relationship between the house prices and macroeconomic indicators. Several macroeconomic indicators are incorporated, such as GDP, unemployment rate, shortterm interest rates, inflation, personal income, investments, immigration, and housing stock. Results show GDP and the housing prices might have a positive correlation. The unemployment rate might affect the income and continue its impact on consumer behaviour. The level of immigration and foreign investments continue to grow indebted. The current negative interest rates are increasing the demand for houses and the housing prices. The housing stock in Portugal is mostly fixed but may experience limited growth as the rebuild program and new constructions. The housing prices in the Algarve and Lisbon are markedly more expensive than the interior. The demand for the different type of houses tended to focus on personal’s need during the recession period. From the regression analysis, the unemployment rate is the closest correlated variable among them. Overall, the housing prices are currently growing and the housing economy and macroeconomic closely related each other.O mercado imobiliário é um segmento essencial de toda a economia do país. Recentemente, o aumento dos preços da habitação em Portugal reflete uma economia em expansão. Este artigo apresenta uma visão macro integrada do mercado imobiliário português com indicadores macroeconómicos. Em primeiro lugar, compara o mercado da habitação e vários indicadores macroeconómicos de 2004 a 2018. Em seguida, inclui-se a análise dinâmica dos preços da habitação por diferentes regiões de Portugal e a sua tipologia. Além disso, o artigo é complementado com a análise de regressão para identificar a relação entre os preços da habitação e os indicadores macroeconômicos. Diversos indicadores macroeconômicos são incorporados, como PIB, taxa de desemprego, taxas de juros de curto prazo, inflação, renda pessoal, investimentos, imigração e estoque habitacional. Os resultados mostram que o PIB e os preços da habitação podem ter uma correlação positiva. A taxa de desemprego pode afetar a renda e continuar seu impacto no comportamento do consumidor. O nível de imigração e investimentos estrangeiros continua endividado. As atuais taxas de juros negativas estão aumentando a demanda por moradias e os preços da moradia. O parque habitacional em Portugal é principalmente fixo, mas pode experimentar um crescimento limitado, como o programa de reconstrução e novas construções. Os preços da habitação no Algarve e em Lisboa são marcadamente mais caros do que o interior. A demanda pelos diferentes tipos de casas tendia a se concentrar nas necessidades pessoais durante o período de recessão. A partir da análise de regressão, a taxa de desemprego é a variável correlacionada mais próxima entre eles. No geral, os preços dos imóveis estão crescendo e a economia imobiliária e macroeconômica estão estreitamente relacionadas

An empirical investigation of the portuguese housing prices: evidence from the period 2004-2018

Housing market is an essential segment of the whole country’s economy. Recently, the rising house prices in Portugal reflects a booming economy. This article presents an integrated macro view of the Portuguese housing market with macroeconomic indicators. Firstly, it compares the housing market and several macroeconomic indicators from 2004 to 2018. Then, the dynamic analysis of the housing prices by different regions in Portugal and its typology included. Also, the article is complemented with the regression analysis to identify the relationship between the house prices and macroeconomic indicators. Several macroeconomic indicators are incorporated, such as GDP, unemployment rate, shortterm interest rates, inflation, personal income, investments, immigration, and housing stock. Results show GDP and the housing prices might have a positive correlation. The unemployment rate might affect the income and continue its impact on consumer behaviour. The level of immigration and foreign investments continue to grow indebted. The current negative interest rates are increasing the demand for houses and the housing prices. The housing stock in Portugal is mostly fixed but may experience limited growth as the rebuild program and new constructions. The housing prices in the Algarve and Lisbon are markedly more expensive than the interior. The demand for the different type of houses tended to focus on personal’s need during the recession period. From the regression analysis, the unemployment rate is the closest correlated variable among them. Overall, the housing prices are currently growing and the housing economy and macroeconomic closely related each other.O mercado imobiliário é um segmento essencial de toda a economia do país. Recentemente, o aumento dos preços da habitação em Portugal reflete uma economia em expansão. Este artigo apresenta uma visão macro integrada do mercado imobiliário português com indicadores macroeconómicos. Em primeiro lugar, compara o mercado da habitação e vários indicadores macroeconómicos de 2004 a 2018. Em seguida, inclui-se a análise dinâmica dos preços da habitação por diferentes regiões de Portugal e a sua tipologia. Além disso, o artigo é complementado com a análise de regressão para identificar a relação entre os preços da habitação e os indicadores macroeconômicos. Diversos indicadores macroeconômicos são incorporados, como PIB, taxa de desemprego, taxas de juros de curto prazo, inflação, renda pessoal, investimentos, imigração e estoque habitacional. Os resultados mostram que o PIB e os preços da habitação podem ter uma correlação positiva. A taxa de desemprego pode afetar a renda e continuar seu impacto no comportamento do consumidor. O nível de imigração e investimentos estrangeiros continua endividado. As atuais taxas de juros negativas estão aumentando a demanda por moradias e os preços da moradia. O parque habitacional em Portugal é principalmente fixo, mas pode experimentar um crescimento limitado, como o programa de reconstrução e novas construções. Os preços da habitação no Algarve e em Lisboa são marcadamente mais caros do que o interior. A demanda pelos diferentes tipos de casas tendia a se concentrar nas necessidades pessoais durante o período de recessão. A partir da análise de regressão, a taxa de desemprego é a variável correlacionada mais próxima entre eles. No geral, os preços dos imóveis estão crescendo e a economia imobiliária e macroeconômica estão estreitamente relacionadas

Induction of MMP-9 release from human dermal fibroblasts by thrombin: involvement of JAK/STAT3 signaling pathway in MMP-9 release

<p>Abstract</p> <p>Background</p> <p>It has been recognized that dermal fibroblasts and matrix metalloproteases (MMP) play crucial roles in wound healing process in skin. Thrombin was found to stimulate IL-8 release from human dermal fibroblasts (HDFs). However, little is known of the effect of thrombin on secretion of MMPs from dermal fibroblasts. In the present study, the influence of thrombin on proMMP-2 and proMMP-9 activity release from primary cultured HDFs, and its potential signaling pathways were investigated.</p> <p>Results</p> <p>The results showed that thrombin induced proMMP-9, but not proMMP-2 release from HDFs in a dose dependent manner at 6 h following incubation. Thrombin also upregulated expression of proMMP-9 mRNA in HDFs. Hirudin completely abolished the action of thrombin on HDFs. An agonist peptide of protease-activated receptor-1, SFLLR-NH₂stimulated an enhanced release of proMMP-9 from HDFs. AG490, an inhibitor of STAT3 inhibited basal and thrombin-provoked proMMP-9 release and phosphorylation of STAT3. PD98059, an inhibitor of MAPK and LY294002, an inhibitor PI3K failed to significantly inhibit thrombin induced proMMP-9 release.</p> <p>Conclusion</p> <p>Thrombin is a potent stimulus of proMMP-9 release from HDFs. Thrombin induced proMMP-9 release is most likely through activation of PAR-1. JAK/STAT3 signaling pathway is involved in proMMP-9 release from HDFs.</p

An Empirical Investigation of the Portuguese Housing Prices (2004-18)

This article presents an integrated macro view of the Portuguese housing market with macroeconomic indicators. Firstly, it compares the housing market and several macroeconomic indicators from 2004 to 2018. Then, the dynamic analysis of the housing prices by different regions in Portugal and its typology included. Also, the article is complemented with the regression analysis to identify the relationship between the house prices and macroeconomic indicators.Results show that the current negative interest rates are increasing the demand for houses and the housing prices. The housing stock in Portugal is mostly fixed but may experience limited growth as the rebuild program and new constructions. GDP and the housing prices have a positive correlation. Houses in Algarve and Lisbon are markedly more expensive than in the interior. From the regression analysis, the unemployment rate is the closest correlated variable

Solving High-Dimensional PDEs with Latent Spectral Models

Deep models have achieved impressive progress in solving partial differential equations (PDEs). A burgeoning paradigm is learning neural operators to approximate the input-output mappings of PDEs. While previous deep models have explored the multiscale architectures and various operator designs, they are limited to learning the operators as a whole in the coordinate space. In real physical science problems, PDEs are complex coupled equations with numerical solvers relying on discretization into high-dimensional coordinate space, which cannot be precisely approximated by a single operator nor efficiently learned due to the curse of dimensionality. We present Latent Spectral Models (LSM) toward an efficient and precise solver for high-dimensional PDEs. Going beyond the coordinate space, LSM enables an attention-based hierarchical projection network to reduce the high-dimensional data into a compact latent space in linear time. Inspired by classical spectral methods in numerical analysis, we design a neural spectral block to solve PDEs in the latent space that approximates complex input-output mappings via learning multiple basis operators, enjoying nice theoretical guarantees for convergence and approximation. Experimentally, LSM achieves consistent state-of-the-art and yields a relative error reduction of 11.5% averaged on seven benchmarks covering both solid and fluid physics

KOBAS server: a web-based platform for automated annotation and pathway identification

There is an increasing need to automatically annotate a set of genes or proteins (from genome sequencing, DNA microarray analysis or protein 2D gel experiments) using controlled vocabularies and identify the pathways involved, especially the statistically enriched pathways. We have previously demonstrated the KEGG Orthology (KO) as an effective alternative controlled vocabulary and developed a standalone KO-Based Annotation System (KOBAS). Here we report a KOBAS server with a friendly web-based user interface and enhanced functionalities. The server can support input by nucleotide or amino acid sequences or by sequence identifiers in popular databases and can annotate the input with KO terms and KEGG pathways by BLAST sequence similarity or directly ID mapping to genes with known annotations. The server can then identify both frequent and statistically enriched pathways, offering the choices of four statistical tests and the option of multiple testing correction. The server also has a ‘User Space’ in which frequent users may store and manage their data and results online. We demonstrate the usability of the server by finding statistically enriched pathways in a set of upregulated genes in Alzheimer's Disease (AD) hippocampal cornu ammonis 1 (CA1). KOBAS server can be accessed at

Sources of variation in simulated ecosystem carbon storage capacity from the 5th Climate Model Intercomparison Project (CMIP5)

Ecosystem carbon (C) storage strongly regulates climate-C cycle feedback and is largely determined by both C residence time and C input from net primary productivity (NPP). However, spatial patterns of ecosystem C storage and its variation have not been well quantified in earth system models (ESMs), which is essential to predict future climate change and guide model development. We intended to evaluate spatial patterns of ecosystem C storage capacity simulated by ESMs as part of the 5th Climate Model Intercomparison Project (CMIP5) and explore the sources of multi-model variation from mean residence time (MRT) and/or C inputs. Five ESMs were evaluated, including C inputs (NPP and [gross primary productivity] GPP), outputs (autotrophic/heterotrophic respiration) and pools (vegetation, litter and soil C). ESMs reasonably simulated the NPP and NPP/GPP ratio compared with Moderate Resolution Imaging Spectroradiometer (MODIS) estimates except NorESM. However, all of the models significantly underestimated ecosystem MRT, resulting in underestimation of ecosystem C storage capacity. CCSM predicted the lowest ecosystem C storage capacity (~10 kg C m−2) with the lowest MRT values (14 yr), while MIROC-ESM estimated the highest ecosystem C storage capacity (~36 kg C m−2) with the longest MRT (44 yr). Ecosystem C storage capacity varied considerably among models, with larger variation at high latitudes and in Australia, mainly resulting from the differences in the MRTs across models. Our results indicate that additional research is needed to improve post-photosynthesis C-cycle modelling, especially at high latitudes, so that ecosystem C residence time and storage capacity can be appropriately simulated

Identification of the ADPR binding pocket in the NUDT9 homology domain of TRPM2

Activation of the transient receptor potential melastatin 2 (TRPM2) channel occurs during the response to oxidative stress under physiological conditions as well as in pathological processes such as ischemia and diabetes. Accumulating evidence indicates that adenosine diphosphate ribose (ADPR) is the most important endogenous ligand of TRPM2. However, although it is known that ADPR binds to the NUDT9 homology (NUDT9-H) domain in the intracellular C-terminal region, the molecular mechanism underlying ADPR binding and activation of TRPM2 remains unknown. In this study, we generate a structural model of the NUDT9-H domain and identify the binding pocket for ADPR using induced docking and molecular dynamics simulation. We find a subset of 11 residues—H1346, T1347, T1349, L1379, G1389, S1391, E1409, D1431, R1433, L1484, and H1488—that are most likely to directly interact with ADPR. Results from mutagenesis and electrophysiology approaches support the predicted binding mechanism, indicating that ADPR binds tightly to the NUDT9-H domain, and suggest that the most significant interactions are the van der Waals forces with S1391 and L1484, polar solvation interaction with E1409, and electronic interactions (including π–π interactions) with H1346, T1347, Y1349, D1431, and H1488. These findings not only clarify the roles of a range of newly identified residues involved in ADPR binding in the TRPM2 channel, but also reveal the binding pocket for ADPR in the NUDT9-H domain, which should facilitate structure-based drug design for the TRPM2 channel