Electron cryomicroscopy (cryo-EM) is essential for the study and functional understanding of non-crystalline macromolecules such as proteins. These molecules cannot be imaged using X-ray crystallography or other popular methods. Cryo-EM has been successfully used to visualize molecules such as ribosomes, viruses, and ion channels, for example. Obtaining structural models of these at various

In 2020 the world has generating 52 times the amount of data as in 2010, and 76 times the number of information sources. Being able to use this data provides huge opportunities and to turn these opportunities into reality, people need to use data to solve problems. Unfortunately in the midst of a global pandemic, when people throughout the world are looking for reliable, trustworthy information about . In this scenario tableau play the vital role, because Tableau is an extremely powerful tool for visualizing massive sets of data very easily. It has an easy to use drag and drop interface. You can build beautiful visualizations easily and in a short amount of time. Tableau supports a wide array of data sources. COVID-19(Coronavirus) analytics with Tableau, you will create dashboards that help you identify the story within our data, and we will better understand the impact of . In this paper comprehensive review about Tableau. The Tableau are the tools which deals with the big data analytics also it generates the output in visualization technique i.e., more understandable and presentable. Its features include data blending, realtime reporting and collaboration of data. In the end, this paper gives the clear picture of growing COVID-19(Coronavirus) data and the tools which can help more effectively, accurately and efficiently.

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Motivation Data-driven deep learning techniques usually require a large quantity of labeled training data to achieve reliable solutions in bioimage analysis. However, noisy image conditions and high cell density in bacterial biofilm images make 3D cell annotations difficult to obtain. Alternatively, data augmentation via synthetic data generation is attempted, but current methods fail to produce realistic images. Results This paper presents a bioimage synthesis and assessment workflow with application to augment bacterial biofilm images. 3D cyclic generative adversarial networks (GAN) with unbalanced cycle consistency loss functions are exploited in order to synthesize 3D biofilm images from binary cell labels. Then, a stochastic synthetic dataset quality assessment (SSQA) measure that compares statistical appearance similarity between random patches from random images in two datasets is proposed. Both SSQA scores and other existing image quality measures indicate that the proposed 3D Cyclic GAN, along with the unbalanced loss function, provides a reliably realistic (as measured by mean opinion score) 3D synthetic biofilm image. In 3D cell segmentation experiments, a GAN-augmented training model also presents more realistic signal-to-background intensity ratio and improved cell counting accuracy. Availability and Implementation https://github.com/jwang-c/DeepBiofilm. Supplementary information Supplementary data are available at Bioinformatics online.

The objective of the research summarized in this manuscript is two-fold: to develop and make available a set of novel analysis tools, fully integrated in ArcGIS, that leverages the rich information provided by satellite-based remote sensing data to detect and characterize geohazards of interest to the transportation community; and to provide a modern web-based decision support system (DSS) in which these novel analysis products can be seamlessly integrated with existing datasets. Specifically, interferometric synthetic aperture radar (InSAR) and its derivatives are employed in this work. Each tool is designed to target a specific asset of interest to the transportation community: sinkhole detection, bridge differential settlement detection, slope stability assessment, and pavement distress evaluation. Furthermore, to allow interested parties to test the introduction of remote sensing data into their decision-making process without requiring the full integration with existing workflows, a modern web-based DSS is introduced. Thanks to the large coverage and the high update frequencies offered by satellite-based remote sensing data, datasets augmented with these new products can offer a near real-time overview of the network status and lead to better support in decisions about resources allocation, existing infrastructure monitoring, new infrastructure planning, and post-construction assessment.