Web GIS to support irrigation management: a prototype for SAGRA network, Alentejo Portugal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAn efficient water management, not only allows significant savings in costs of irrigation, but also an effective control on the quality of products, which can have obvious consequences on income operation and reducing the environmental impact of irrigation. As the Internet is becoming the easiest way of information distribution, irrigation management system can also be benefitted with it. Integrating GIS functionality with internet capacity will redefine the way of decision making, sharing and processing of information. In irrigation systems weather plays an imperative role in decision making, implementing and forecasting. Temperature, humidity, precipitation, and solar radiation are the most important parameters to calculate evapotranspiration by which crop water requirement can be determined. SAGRA (Sistema Agrometeorológico para a Gestão da Rega no Alentejo) network is providing information to the farmers through web but still lacks the use of GIS in their information to decision support system. Irrigation management support system can be benefitted with the use of Web GIS. In this thesis, web based GIS is designed using popular open source tools and software. Using data from automatic weather station maps are produced using Geo-statistical interpolation techniques and published in web map. These maps can be viewed with popular online maps like Google maps, Microsoft Bing and Openstreet maps. Animated weather maps are also created which are useful for visualizing changing pattern of weather parameters and water requirement over time

A large expert-curated cryo-EM image dataset for machine learning protein particle picking

Abstract Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking