scholarly journals The BioImage Archive - home of life-sciences microscopy data

2021 ◽  
Author(s):  
Matthew Hartley ◽  
Gerard Kleywegt ◽  
Ardan Patwardhan ◽  
Ugis Sarkans ◽  
Jason R Swedlow ◽  
...  
Keyword(s):  
Structural Biology ◽  
Imaging Modality ◽  
Image Data ◽  
Biological Data ◽  
Analysis Tool ◽  
Tool Development ◽  
Data Resource ◽  
Microscopy Data ◽  
Downstream Analysis ◽  
Central Archive

Despite the importance of data resources in genomics and structural biology, until now there has been no central archive for biological data for all imaging modalities. The BioImage Archive is a new data resource at the European Bioinformatics Institute (EMBL-EBI) designed to fill this gap. It accepts bioimaging data associated with publication in any format, from any imaging modality at any scale, as well as reference datasets. The BioImage Archive will improve reproducibility of published studies that derive results from image data. In addition, providing reference datasets to the scientific community reduces duplication of effort and allows downstream analysis to focus on a consistent set of data. The BioImage Archive will also help to generate new insights through reuse of existing data to answer new biological questions, or provision of training, testing and benchmarking data for image analysis tool development. The Archive is available at https://www.ebi.ac.uk/bioimage-archive/.

scholarly journals A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Johannes Jordan ◽  
Elli Angelopoulou ◽  
Andreas Maier
Keyword(s):  
Hyperspectral Image ◽  
Imaging Modality ◽  
High Capacity ◽  
Interactive Visualization ◽  
Image Data ◽  
Hyperspectral Image Analysis ◽  
Supervised Segmentation ◽  
Hyperspectral Image Data ◽  
Visualization Techniques ◽  
Application Specific

Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries.

scholarly journals Towards the Development of Language Analysis Tools for the Written Latgalian Language

2020 ◽  
Author(s):  
Daiga Deksne ◽  
Anna Vulāne
Keyword(s):  
Web Service ◽  
Morphological Analysis ◽  
Written Language ◽  
Analysis Tool ◽  
Tool Development ◽  
Joint Effort ◽  
Analysis Tools ◽  
Language Analysis ◽  
Wide Range ◽  
Development Of Language

This paper reports on the development of spell checking and morphological analysis tools for Latgalian. The Latgalian written language is a historic variant of the Latvian language. There is a wide range of language analysis tools available for Latvian, whereas the Latgalian language lacks such tools. The work is done by the joint effort of linguists who work on morphologically marked lexicon creation and IT specialists who work on language tool development. For the creation of a morphological analysis tool, we reuse the FST technology used for the Latvian morphological analyzer. We create a spelling dictionary that can be used with the Hunspell engine. All tools are accessible via Web Service. For now, the Latgalian lexicon contains 13,139 lemmas marked by 105 inflection groups. The work of lexicon replenishment still continues.

scholarly journals The data universe of structural biology

IUCrJ ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 630-638 ◽  
Author(s):  
Helen M. Berman ◽  
Brinda Vallat ◽  
Catherine L. Lawson
Keyword(s):  
Protein Data Bank ◽  
Structural Biology ◽  
Data Bank ◽  
Small Data ◽  
Complex Structures ◽  
Data Resource ◽  
History Of

The Protein Data Bank (PDB) has grown from a small data resource for crystallographers to a worldwide resource serving structural biology. The history of the growth of the PDB and the role that the community has played in developing standards and policies are described. This article also illustrates how other biophysics communities are collaborating with the worldwide PDB to create a network of interoperating data resources. This network will expand the capabilities of structural biology and enable the determination and archiving of increasingly complex structures.

scholarly journals Advanced Cluster and Predictive Analysis Tool Development for Commercial Office Real Estate Energy Usage

2021 ◽  
Author(s):  
Carleen Lawson
Keyword(s):  
Energy Use ◽  
Data Gathering ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Tool Development ◽  
Energy Usage ◽  
Future Data ◽  
Building Characteristics ◽  
Energy Benchmarking ◽  
Physical Features

From 2009-2015, REALPAC collected monthly energy usage and building characteristics for over 500 buildings in the 20 by ‘15 Energy Benchmarking Survey (REALPAC, 2009). While preliminary analysis had been completed on this dataset, this research undertook an in-depth statistical analysis of the data to identify trends and important variables. Eight machine learning algorithms were employed to predict energy usage as a function of previous energy use and select physical features. The dataset did not possess the appropriate variables to predict such usage accurately. Characteristics such as building system efficiency, construction assemblies, condition, compactness, and window to wall ratio are thus recommended for inclusion in future data-gathering initiatives. https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW_RSCR-4.80MB/view https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW-ExTa-428KB/view https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW-ExGa-5.62MB/view https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW-DATA-1.9MB/view

Use of digital photography to identify neoplastic skin lesions after labelling by ALA-derived protoporphyrin

2021 ◽  
pp. A-G
Author(s):  
Hyunseon Yu ◽  
Tien Son Ho ◽  
Heesung Kang ◽  
Youngwoo Bae ◽  
Eung Ho Choi ◽  
...  
Keyword(s):  
Actinic Keratosis ◽  
Aminolevulinic Acid ◽  
Imaging Modality ◽  
Normal Skin ◽  
Protoporphyrin Ix ◽  
Image Data ◽  
Skin Area ◽  
Clinical Images ◽  
Actinic Keratosis Lesion ◽  
Fluorescence Images

Actinic keratosis is a premalignant skin lesion that develops into non-melanoma skin cancer. Various imaging techniques have been developed to find the actinic keratosis lesion. In this clinical study, the feasibility of a nonspectroscopic fluorescence imaging system is investigated for spatial assessment of the actinic keratosis lesion. Six patients between the ages of 70 and 80 years old are diagnosed with actinic keratosis by a board-certified dermatologist to obtain biopsy-proven clinical images. The patients were treated with 5-aminolevulinic acid, which is transformed into the protoporphyrin IX. After illuminating ultraviolet-A light on facial lesions, the protoporphyrin IX produces the exogenous fluorescence. The fluorescence is measured using both a hyperspectral camera and an RGB color camera to obtain spectroscopic and nonspectroscopic fluorescence images, respectively. It is found that fluorescence intensity of the actinic keratosis lesion is higher than that of normal skin. Based on combined fluorescence and physiological characteristics, the actinic keratosis lesion is distinguished from the adjacent normal skin area. For delineation of the actinic keratosis lesion, a linear unmixing algorithm is applied to spectroscopic image data and an erythema index is calculated from nonspectroscopic image data. Then, two extracted actinic keratosis lesions are compared for cross-validation. As a result, both spectroscopic and nonspectroscopic fluorescence images demarcate an identical lesion of actinic keratosis. Given the affordability and simplicity, an RGB camera and a 5-ALA photosensitizer can be used as a cost-effective nonspectroscopic imaging modality for accurate assessment of actinic keratosis margins.

R*-Tree Based Similarity and Clustering Analysis for Images

2011 ◽  
pp. 50-61
Author(s):  
Jiaxiong Pi ◽  
Yong Shi ◽  
Zhengxin Chen
Keyword(s):  
Cluster Analysis ◽  
Spatial Data ◽  
Image Data ◽  
Multimedia Retrieval ◽  
Spatial Proximity ◽  
Similarity Analysis ◽  
Analysis Tool ◽  
Clustering Methods ◽  
Hierarchical Clustering Methods ◽  
And Cluster Analysis

Image content analysis plays an important role for adaptive multimedia retrieval. In this chapter, the authors present their work on using a useful spatial data structure, R*-tree, for similarity analysis and cluster analysis of image contents. First, they describe an R*-tree based similarity analysis tool for similarity retrieval of images. They then move on to discuss R*-tree based clustering methods for images, which has been a tricky issue: although objects stored in the same R* tree leaf node enjoys spatial proximity, it is well-known that R* trees cannot be used directly for cluster analysis. Nevertheless, R* tree’s indexing feature can be used to assist existing cluster analysis methods, thus enhancing their performance of cluster quality. In this chapter, the authors report their progress of using R* trees to improve well-known K-means and hierarchical clustering methods. Based on R*-Tree’s feature of indexing Minimum Bounding Box (MBB) according to spatial proximity, the authors extend R*-Tree’s application to cluster analysis containing image data. Two improved algorithms, KMeans-R and Hierarchy-R, are proposed. Experiments have shown that KMeans-R and Hierarchy-R have achieved better clustering quality.

scholarly journals EM∩IM: software for relating ion mobility mass spectrometry and electron microscopy data

The Analyst ◽  
2016 ◽  
Vol 141 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Matteo T. Degiacomi ◽  
Justin L. P. Benesch
Keyword(s):  
Electron Microscopy ◽  
Mass Spectrometry ◽  
Structural Biology ◽  
Cross Sections ◽  
Ion Mobility ◽  
Ion Mobility Mass Spectrometry ◽  
Density Maps ◽  
Transmission Electron ◽  
Microscopy Data ◽  
Means Of Transmission

EM∩IM enables the calculation of collision cross-sections from electron density maps obtained, for example, by means of transmission electron microscopy. This capability will further aid the integration of ion mobility mass spectrometry with modern structural biology.

scholarly journals Structural Biology and Molecular Modeling to Analyze the Entry of Bacterial Toxins and Virulence Factors into Host Cells

Toxins ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 369 ◽  
Author(s):  
Irène Pitard ◽  
Thérèse E Malliavin
Keyword(s):  
Molecular Modeling ◽  
Structural Biology ◽  
Virulence Factors ◽  
Structural Information ◽  
Biological Data ◽  
Bacterial Toxins ◽  
Host Cells ◽  
Botulinum Toxins ◽  
Edema Factor ◽  
Dynamical Properties

Understanding the functions and mechanisms of biological systems is an outstanding challenge. One way to overcome it is to combine together several approaches such as molecular modeling and experimental structural biology techniques. Indeed, the interplay between structural and dynamical properties of the system is crucial to unravel the function of molecular machinery’s. In this review, we focus on how molecular simulations along with structural information can aid in interpreting biological data. Here, we examine two different cases: (i) the endosomal translocation toxins (diphtheria, tetanus, botulinum toxins) and (ii) the activation of adenylyl cyclase inside the cytoplasm (edema factor, CyA, ExoY).

scholarly journals Evolving Strategies for the Incorporation of Bioinformatics Within the Undergraduate Cell Biology Curriculum

2003 ◽  
Vol 2 (4) ◽  
pp. 233-247 ◽  
Author(s):  
Jerry E. Honts
Keyword(s):  
Cell Biology ◽  
Biological Data ◽  
Point Of View ◽  
Analysis Tool ◽  
Undergraduate Biology ◽  
Biology Students ◽  
Introductory Course ◽  
Sequence Analysis Tool ◽  
Biology Curriculum ◽  
Simple Sequence

Recent advances in genomics and structural biology have resulted in an unprecedented increase in biological data available from Internet-accessible databases. In order to help students effectively use this vast repository of information, undergraduate biology students at Drake University were introduced to bioinformatics software and databases in three courses, beginning with an introductory course in cell biology. The exercises and projects that were used to help students develop literacy in bioinformatics are described. In a recently offered course in bioinformatics, students developed their own simple sequence analysis tool using the Perl programming language. These experiences are described from the point of view of the instructor as well as the students. A preliminary assessment has been made of the degree to which students had developed a working knowledge of bioinformatics concepts and methods. Finally, some conclusions have been drawn from these courses that may be helpful to instructors wishing to introduce bioinformatics within the undergraduate biology curriculum.

A Deep Learning Method for Limited-View Intravascular Photoacoustic Image Reconstruction

2020 ◽  
Vol 10 (11) ◽  
pp. 2707-2713
Author(s):  
Zheng Sun ◽  
Xiangyang Yan
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Imaging Modality ◽  
Image Data ◽  
Structural Similarity ◽  
Simulated Image ◽  
Cross Sectional ◽  
Data Set ◽  
Reconstruction Methods ◽  
Under Sampling

Intravascular photoacoustic tomography (IVPAT) is a newly developed imaging modality in the interventional diagnosis and treatment of coronary artery diseases. Incomplete acoustic measurement caused by limitedview scanning of the detector in the vascular lumen results in under-sampling artifacts and distortion in the images reconstructed by using the standard reconstruction methods. A method for limited-view IVPAT image reconstruction based on deep learning is presented in this paper. A convolutional neural network (CNN) is constructed and trained with computer-simulated image data set. Then, the trained CNN is used to optimize the cross-sectional images of the vessel which are recovered from the incomplete photoacoustic measurements by using the standard time-reversal (TR) algorithm to obtain the images with the improved quality. Results of numerical demonstration indicate that the method can effectively reduce the image distortion and artifacts caused by the limited-view detection. Furthermore, it is superior to the compressed sensing (CS) method in recovering the unmeasured information of the imaging target with the structural similarity around 10% higher than CS reconstruction.

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