Dataset consists of 5 files.

First 4 files describe protein-protein interactions(PPI) in homo sapiens. They are obtained from IntAct database(http://www.ebi.ac.uk/intact/).Selected interaction are filtered based on MI score >= 0.45 from IntAct and considered to be highly confident.Protein names are translated to unique Ensemble(http://www.ensembl.org/index.html) gene identifiers (ENSG). 1. Dataset containing expert curated interactions related to Parkinson's disease parkinson_intact_int_PPI.txt2. All highly confident interactions in hs available in IntAct database intact_int.txt3. Aumatically curated interactions related to synaptic activity synapse_intact_int.txt4. Dataset containing expert curated interactions related to Alzheimer's diseasealz_intact_int_PPI.txtThe 5th file describes genes, that are coexpressed in the Alzheimer's patients and healthy individuals.Correlation profiles are calculated using the set of microarray data obtained from ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/). Final coexpression score is calculated by applying Robust Rank Aggregation method to aggregate correlation scores in individual microarray datasets.Coexpression scores are filtered based on pvalue

Producer Prices in Japan increased 4 percent in February of 2025 over the same month in the previous year. This dataset provides - Japan Producer Prices Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

BackgroundDue to their efficacy and tolerability, utilization of proton pump inhibitors (PPI) has significantly increased worldwide. Parallel to the clinical benefits, potential long-term side effects have been observed, which, along with increasing medical expenses and potential drug interactions, justifies the analysis of the trends of utilization.ObjectiveThe aim of the present study was to show the level, pattern, and characteristics of PPI use.MethodsWe assessed the nationwide use of proton pump inhibitors in ambulatory care based on aggregated utilization data from the National Health Insurance database. The annual PPI utilization was expressed as the number of packages and as number of DDDs per 1,000 inhabitants and per year. For 2018, we estimated PPI exposure as the number of packages and as the number of DDDs per user per year. The annual reimbursement costs of proton pump inhibitors were also calculated. Moreover, three patient-level surveys were carried out in non-gastroenterological inpatient hospital departments to reveal characteristics of proton pump inhibitor use, namely dose, duration, and indication.ResultsThe PPI utilisation increased from 5867.8 thousand to 7124.9 thousand packages and from 41.9 to 50.4 DDD per 1,000 inhabitants and per day between 2014 and 2018. Nationwide data showed that 14% of the adult population was exposed to proton pump inhibitors in 2018, while among hospitalized patients, the prevalence of proton pump inhibitor use was between 44.5% and 54.1%. Pantoprazole was the most frequently used active ingredient, both in the nationwide data and in the patient-level surveys. In the patient-level survey in majority of patients (71.5%–80.0%) proton pump inhibitors were prescribed for prophylaxis. Many inpatients (29.4%–36.9%) used 80 mg pantoprazole per day. The average number of PPI packages per user was 6.5 in 2018 in the nationwide data. The duration of PPI therapy was typically between 1 and 5 years in the patient-level surveys and nearly 20% of the inpatients had been taking proton pump inhibitors for more than 5 years.ConclusionsOur data suggests that Hungarian patients receive proton pump inhibitors in high doses and for a long time. Use of proton pump inhibitors beyond their recommended indications was also found.

This table shows the price indices, quarterly and yearly changes in prices of services that companies provide. The figures are broken down by type of services according to the Classification of Products by Activity (CPA 2008). For some services, a further breakdown has been made on the basis of market data that differ from the CPA. This breakdown is indicated with a letter after the CPA code.

The base year for all Services producer price indices is 2015. The year average, quarterly and yearly changes are calculated with unrounded figures.

Data available from: 4th quarter 2002 up to and including 2023.

Status of the figures: The figures in this table are final.

Changes as of May 17 2024: None, this table is stopped.

When will new figures be published? Not applicable anymore. This table was discontinued on May 17, 2024 and continued as Services producer price index (SPPI); index 2021=100. See paragraph 3.

The Services producer price indices publication schedule can be downloaded as an Excel file under section: 3 Relevant articles. More information about the pricing method can be found in the video under section: 3 Relevant articles.

Site Description: In this dataset, there are seventeen production crop fields in Bulgaria where winter rapeseed and wheat were grown and two research fields in France where winter wheat – rapeseed – barley – sunflower and winter wheat – irrigated maize crop rotation is used. The full description of those fields is in the database "In-situ crop phenology dataset from sites in Bulgaria and France" (doi.org/10.5281/zenodo.7875440). Methodology and Data Description: Remote sensing data is extracted from Sentinel-2 tiles 35TNJ for Bulgarian sites and 31TCJ for French sites on the day of the overpass since September 2015 for Sentinel-2 derived vegetation indices and since October 2016 for HR-VPP products. To suppress spectral mixing effects at the parcel boundaries, as highlighted by Meier et al., 2020, the values from all datasets were subgrouped per field and then aggregated to a single median value for further analysis. Sentinel-2 data was downloaded for all test sites from CREODIAS (https://creodias.eu/) in L2A processing level using a maximum scene-wide cloudy cover threshold of 75%. Scenes before 2017 were available in L1C processing level only. Scenes in L1C processing level were corrected for atmospheric effects after downloading using Sen2Cor (v2.9) with default settings. This was the same version used for the L2A scenes obtained intermediately from CREODIAS. Next, the data was extracted from the Sentinel-2 scenes for each field parcel where only SCL classes 4 (vegetation) and 5 (bare soil) pixels were kept. We resampled the 20m band B8A to match the spatial resolution of the green and red band (10m) using nearest neighbor interpolation. The entire image processing chain was carried out using the open-source Python Earth Observation Data Analysis Library (EOdal) (Graf et al., 2022). Apart from the widely used Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), we included two recently proposed indices that were reported to have a higher correlation with photosynthesis and drought response of vegetation: These were the Near-Infrared Reflection of Vegetation (NIRv) (Badgley et al., 2017) and Kernel NDVI (kNDVI) (Camps-Valls et al., 2021). We calculated the vegetation indices in two different ways: First, we used B08 as near-infrared (NIR) band which comes in a native spatial resolution of 10 m. B08 (central wavelength 833 nm) has a relatively coarse spectral resolution with a bandwidth of 106 nm. Second, we used B8A which is available at 20 m spatial resolution. B8A differs from B08 in its central wavelength (864 nm) and has a narrower bandwidth (21 nm or 22 nm in the case of Sentinel-2A and 2B, respectively) compared to B08. The High Resolution Vegetation Phenology and Productivity (HR-VPP) dataset from Copernicus Land Monitoring Service (CLMS) has three 10-m set products of Sentinel-2: vegetation indices, vegetation phenology and productivity parameters and seasonal trajectories (Tian et al., 2021). Both vegetation indices, Normalized Vegetation Index (NDVI) and Plant Phenology (PPI) and plant parameters, Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) and Leaf Area Index (LAI) were computed for the time of Sentinel-2 overpass by the data provider. NDVI is computed directly from B04 and B08 and PPI is computed using Difference Vegetation Index (DVI = B08 - B04) and its seasonal maximum value per pixel. FAPAR and LAI are retrieved from B03 and B04 and B08 with neural network training on PROSAIL model simulations. The dataset has a quality flag product (QFLAG2) which is a 16-bit that extends the scene classification band (SCL) of the Sentinel-2 Level-2 products. A “medium” filter was used to mask out QFLAG2 values from 2 to 1022, leaving land pixels (bit 1) within or outside cloud proximity (bits 11 and 13) or cloud shadow proximity (bits 12 and 14). The HR-VPP daily raw vegetation indices products are described in detail in the user manual (Smets et al., 2022) and the computations details of PPI are given by Jin and Eklundh (2014). Seasonal trajectories refer to the 10-daily smoothed time-series of PPI used for vegetation phenology and productivity parameters retrieval with TIMESAT (Jönsson and Eklundh 2002, 2004). HR-VPP data was downloaded through the WEkEO Copernicus Data and Information Access Services (DIAS) system with a Python 3.8.10 harmonized data access (HDA) API 0.2.1. Zonal statistics [’min’, ’max’, ’mean’, ’median’, ’count’, ’std’, ’majority’] were computed on non-masked pixel values within field boundaries with rasterstats Python package 0.17.00. The Start of season date (SOSD), end of season date (EOSD) and length of seasons (LENGTH) were extracted from the annual Vegetation Phenology and Productivity Parameters (VPP) dataset as an additional source for comparison. These data are a product of the Vegetation Phenology and Productivity Parameters, see (https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity/vegetation-phenology-and-productivity) for detailed information. File Description: 4 datasets: 1_senseco_data_S2_B08_Bulgaria_France; 1_senseco_data_S2_B8A_Bulgaria_France; 1_senseco_data_HR_VPP_Bulgaria_France; 1_senseco_data_phenology_VPP_Bulgaria_France 3 metadata: 2_senseco_metadata_S2_B08_B8A_Bulgaria_France; 2_senseco_metadata_HR_VPP_Bulgaria_France; 2_senseco_metadata_phenology_VPP_Bulgaria_France The dataset files “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” concerns all vegetation indices (EVI, NDVI, kNDVI, NIRv) data values and related information, and metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France” describes all the existing variables. Both “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” have the same column variable names and for that reason, they share the same metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France”. The dataset file “1_senseco_data_HR_VPP_Bulgaria_France” concerns vegetation indices (NDVI, PPI) and plant parameters (LAI, FAPAR) data values and related information, and metadata file “2_senseco_metadata_HRVPP_Bulgaria_France” describes all the existing variables. The dataset file “1_senseco_data_phenology_VPP_Bulgaria_France” concerns the vegetation phenology and productivity parameters (LENGTH, SOSD, EOSD) values and related information, and metadata file “2_senseco_metadata_VPP_Bulgaria_France” describes all the existing variables. Bibliography G. Badgley, C.B. Field, J.A. Berry, Canopy near-infrared reflectance and terrestrial photosynthesis, Sci. Adv. 3 (2017) e1602244. https://doi.org/10.1126/sciadv.1602244. G. Camps-Valls, M. Campos-Taberner, Á. Moreno-Martínez, S. Walther, G. Duveiller, A. Cescatti, M.D. Mahecha, J. Muñoz-Marí, F.J. García-Haro, L. Guanter, M. Jung, J.A. Gamon, M. Reichstein, S.W. Running, A unified vegetation index for quantifying the terrestrial biosphere, Sci. Adv. 7 (2021) eabc7447. https://doi.org/10.1126/sciadv.abc7447. L.V. Graf, G. Perich, H. Aasen, EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data, Comput. Electron. Agric. 203 (2022) 107487. https://doi.org/10.1016/j.compag.2022.107487. H. Jin, L. Eklundh, A physically based vegetation index for improved monitoring of plant phenology, Remote Sens. Environ. 152 (2014) 512–525. https://doi.org/10.1016/j.rse.2014.07.010. P. Jonsson, L. Eklundh, Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Trans. Geosci. Remote Sens. 40 (2002) 1824–1832. https://doi.org/10.1109/TGRS.2002.802519. P. Jönsson, L. Eklundh, TIMESAT—a program for analyzing time-series of satellite sensor data, Comput. Geosci. 30 (2004) 833–845. https://doi.org/10.1016/j.cageo.2004.05.006. J. Meier, W. Mauser, T. Hank, H. Bach, Assessments on the impact of high-resolution-sensor pixel sizes for common agricultural policy and smart farming services in European regions, Comput. Electron. Agric. 169 (2020) 105205. https://doi.org/10.1016/j.compag.2019.105205. B. Smets, Z. Cai, L. Eklund, F. Tian, K. Bonte, R. Van Hoost, R. Van De Kerchove, S. Adriaensen, B. De Roo, T. Jacobs, F. Camacho, J. Sánchez-Zapero, S. Else, H. Scheifinger, K. Hufkens, P. Jönsson, HR-VPP Product User Manual Vegetation Indices, 2022. F. Tian, Z. Cai, H. Jin, K. Hufkens, H. Scheifinger, T. Tagesson, B. Smets, R. Van Hoolst, K. Bonte, E. Ivits, X. Tong, J. Ardö, L. Eklundh, Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe, Remote Sens. Environ. 260 (2021) 112456. https://doi.org/10.1016/j.rse.2021.112456.

Key information about New Zealand Producer Price Index Growth

• New Zealand Producer Price Index (PPI) grew 5.3 % YoY in Mar 2023, compared with a growth of 7.7 % YoY in the previous quarter.
• New Zealand Producer Price Index data is updated quarterly, available from Dec 1978 to Mar 2023, with an average change of 3.4 % YoY.
• The data reached an all-time high of 19.3 % YoY in Mar 1980 and a record low of -3.6 % YoY in Dec 2009.

The raster file is the temporal trend in the start of the vegetation growing season. The start of growing season dataset is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The start of season dataset is based on calculating the start of the growing season from the annual PPI temporal curve using the TIMESAT software. Change in the start of growing season was then calculated after fitting a linear trend and extracting the slope of the trend. Negative values refer to earlier start of the vegetation growing season, whereas positive values refer to later start of the vegetation growing season.

Key information about Russia Producer Price Index Growth

• Russia Producer Price Index (PPI) grew 9.7 % YoY in Jan 2025, compared with a growth of 7.9 % YoY in the previous month.
• Russia Producer Price Index data is updated monthly, available from Jan 2013 to Jan 2025, with an average change of 6.7 % YoY.
• The data reached an all-time high of 35.3 % YoY in May 2021 and a record low of -14.1 % YoY in May 2020.

Construction Output Price Indices (OPIs) from January 2014 to December 2024, UK. Summary.

The top 10 genes in the PPI network were calculated using five algorithms.

Consumer Price Index CPI in Pakistan decreased to 263.95 points in February from 266.17 points in January of 2025. This dataset provides - Pakistan Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.