Grid Computing in 3D-EM Image Processing Using Xmipp

Image processing in electron microscopy : Non-periodic objects

Density correction of photographic material for further image processing in electron microscopy

The Electron Microscopy eXchange (EMX) initiative.

A general account of the reasons why digital processing of electron microscope images is of interest is given, with references to a wide range of specific applications. Emphasis is placed on the stimulus provided by the radiation damage problem. Some brief comments on image readout in TEM and STEM are included.KeywordsElectron MicrographsRadiation DamageElectron Microscope ImageModulation Transfer FunctionDigital Image ProcessingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Computer Techniques for Image Processing in Electron Microscopy

Grid computing provides a high performance computing platform to solve larger scale applications by coordinating and sharing computational power, data storage and network resources across dynamic and geographically dispersed organizations. Scheduling onto the Grid is NP-complete, so there is no best scheduling algorithm for all grid computing systems. An alternative is to select an appropriate scheduling algorithm to use in a given grid environment because of the characteristics of the tasks, machines and network connectivity. Job scheduling is one of the key research area in grid computing. The goal of scheduling is to achieve highest possible system throughput and to match the application need with the available computing resources. Motivation of this study is to encourage and help the amateur researcher in the field of grid computing, so that they can understand easily the concept of scheduling and can contribute in developing more efficient and practical scheduling algorithm. This will benefit interested researchers to carry out further work in this thrust area of research.

In this paper, we review and summarize some of the leading computing concepts including cluster, grid, and cloud computing. These ideas relate to bringing computers together to build a more powerful computational engine that is capable of processing multiple tasks concurrently. In cluster computing, a group of computers are located close by and directly linked with extremely high speed connections in order to perform as a single computer system. In grid computing, computer resources from multiple Internet domains are combined to reach a common computational goal. Compared with clusters, grids tend to be more loosely coupled, heterogeneous, and geographically distributed. Cloud computing is an emerging technology allowing a large number of interconnected computers, hardware and/or software to be rented as a commodity like electricity. The service types of cluster, grid, and cloud have been compared. We conclude that cloud computing has the potential to meet various computing demands in a cost effective easy way provided that some crucial issues like privacy, security, migration, and management are properly addressed.

Grid computing is the combination of computer resources from multiple administrative domains for a common goal. Grid computing (or the use of a computational grid) is applying the resources of many computers in a network to a single problem at the same time - usually to solve a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of data. One of the main strategies of Grid computing is to use middleware to divide and apportion pieces of a program among several computers, sometimes up to many thousands. Grid computing involves computation in a distributed fashion, which may also involve the aggregation of large-scale cluster computing based systems. The size of a Grid may vary from small - confined to a network of computer workstations within a corporation, for example - to being large, public collaboration across many companies and networks. "The notion of a confined grid may also be known as intra-nodes cooperation whilst the notion of a larger, wider grid may thus refer to inter-nodes cooperation". Since the spectrum of grid computing is too much broad it is very much important to balance the load among the various nodes of the network. Various load balancing algorithms were developed assuming homogenous set of nodes linked with homogenous and fast networks. In this paper we studies the various load balancing strategies that control the load of the entire system.

With a wealth of biodiversity, a long tradition of agriculture-based industries, and an established medical and biotechnological research and development community, Thailand has become an attractive location for life sciences investment. The large amount of data generated in many areas of life sciences requires visualization, management, and analysis, principally through bioinformatics. To become successful, Thailand's research community should emphasize establishing core technologies, such as genomics and bioinformatics, to boost development of agriculture, food processing, and biomedical research. The Thai government realized the importance of this field and created a national policy to greatly increase Thailand's participation in bioinformatics and genomics, budgeting for specific development goals in research infrastructure, education, and sustainable human resources. Thailand has not lagged behind in bioinformatics research activity and recognizes the importance of bioinformatics through increased policy awareness, human resources development, and increased research activity involving genomic-scale data generation and computational analyses. Many applications of genomics and bioinformatics to biomedical research and development in Thailand have progressed substantially during the past few years, leading to successful applications in some specific local areas. However, the applications to other important areas, such as agriculture, are hampered by the limited availability of genomic sequence data and the lack of necessary biochemical/physiological information. With the advent of more and more genomic information in public databases, Thailand's research community is striving to adopt comparative genomics to obtain information of direct relevance to the country's health and industrial needs. This article highlights Thailand's contribution to genomics and bioinformatics in the following areas: (1) policy support from the Thai government, (2) capacity building through infrastructure/education/human resources, and (3) research and development in genomics and computational biology. (See Box 1 for Authors' Biographies). Box 1. Authors' Biographies Wannipha Tongsima, M.S., obtained her master's degree in Industrial Microbiology from Chulalongkorn University, Thailand. She was involved in founding the Bioinformatics research program in BIOTEC. To reinforce the research activity in this area, she also helped organize the first International Conference on Computational Biology (InCoB), held in Bangkok in 2002. Later, she was appointed to manage one of the first BIOTEC ethnic-specific human genetic variation programs, named the Thailand SNP Discovery Project. She works as a Genomic Medicine program coordinator for the Cluster and Program Management Office (CPMO) of the National Science and Technology Development Agency (NSTDA), which is an umbrella organization of four other national research centers in Thailand, including BIOTEC. Sissades Tongsima, Ph.D., received his doctoral degree in Computer Science and Engineering from the University of Notre Dame, Indiana, United States. He has worked for the National Electronics and Computer Technology Center on High Performance Computing (HPC) and Computational Grid. During 2002–2004, he cochaired the Asia-Pacific Advanced Network (APAN) Grid Working Group. In 2003, he shifted his research direction from HPC architecture to bioinformatics research, when he started working for BIOTEC, and constructed the ThaiSNP database. His main research interest is in developing algorithms and databases for analyzing various research projects on human genetic variation. He currently heads the Genome Institute biostatistics and informatics laboratory at BIOTEC. Prasit Palittapongarnpim, M.D., earned his medical degree from Mahidol University, Thailand, and his B.S. in Mathematics from Ramkumhang University, Thailand. He is a Fellow of the Royal College of Pediatricians of Thailand and also an Associate Professor in Microbiology at Mahidol University, where he has conducted research focusing on tuberculosis. While holding a Deputy Director position, he initiated the Bioinformatics research program at BIOTEC in 2002 and led the organization of the first InCoB conference in 2002. He is currently a Vice President of NSTDA.

Purpose The dicentric chromosome assay (DCA), recognized as the gold standard for biological dosimetry in emergency radiation exposure, involves multiple key steps. Among these, metaphase image selection remains ambiguous and particularly challenging for beginners. This study evaluates the impact of metaphase image selection on dicentric (Dic) frequency during initial training. Materials and methods Peripheral blood (PB) from three healthy donors was irradiated with 2 Gy X-rays and incubated at 37 °C for 2 h for DNA repair. PB samples were then cultured for 48 h with phytohemagglutinin and colcemid. After fixation, metaphase spreads were prepared, Giemsa-stained, and images were captured by specific microscope imaging and image processing software. Three beginners independently selected and analyzed 50 metaphase images each. A trainer with experience in performing radiation emergency related DCA then reviewed and removed unsuitable images. The beginners reanalyzed Dic frequencies, supplementing with additional images as needed to reach 50 metaphases. Dic frequencies were compared before and after image refinement. Results Dic frequencies were lower in metaphase image pools selected by beginners compared to those refined by the trainer. The novices’ selections included numerous over-condensed metaphases, making Dic detection difficult. Conclusion Blood culture for 48 h permanent colcemid treatment increases the occurrence of over-condensed chromosomes, affecting Dic scoring. This study highlights the importance of proper training in metaphase image selection and structured education on quality assessment, including the use of specific microscope imaging and image processing software to ensure accurate biological dosimetry.

Deep Learning (DL) revolutionized a wide variety of research fields and applications of Artificial Intelligence (AI) in the last years. Computer vision is among those fields that witnessed a stronger impact, especially in some tasks like image equalization, classification and segmentation, that have several applications in medical imaging. Such applications often focus on microscopy images, but they can be successfully extended to other fields, like the detection of diseases involving the ocular surface and the cornea.One of the reasons of the success of deep learning applied to image processing relies in the end‐to‐end data‐driven approach. This means that the whole image processing and classification/segmentation pipeline can be adapted to the data being processed. This is particularly powerful when dealing with medical images, whose processing is often based on the detection of low‐ and mid‐level patterns that might be difficult to define in a formal manner. This means that deep learning has the chance to select and consider some image elements that would be nearly impossible to define using standard computer vision techniques. However, this becomes possible if large amounts of data are available for training the network, which became reality in the last years thanks to the wide digitalization of medical systems, including ophthalmology equipment.Based on a substantial amount of literature that has rapidly grown in the last years, this talk will focus on the new perspectives of eye disease diagnosis that were opened by the advent of deep learning. At the same time, the limitations and risks that must be considered in the development of DL‐based medical systems, like overfitting and lack of generalization, will be highlighted and discussed. Keeping track of such limitations is crucial in order not to consider DL as a sort of magic box, but rather, as powerful image analysis and processing tool.The talk will also provide a qualitative and quantitative evaluation of the diagnosis systems currently available that exploit DL and AI systems. The current and future impact of automatic image processing systems dedicated to ophthalmology will also be discussed. When automatic medical image analysis will be widely available, the patterns of medical diagnosis will need to change, taking advantage of the possibility to perform larger screenings that will be automatically processed.

The current development of high performance parallel supercomputing infrastructures are pushing the boundaries of applications of science and are bringing new paradigms into engineering practices and simulations. Earthquake engineering is also one of the major fields, which benefits from above by looking for solutions in grid computing and cloud computing techniques. Generally, earthquake simulations involve analysis of petabytes of data. Analyzing these large amounts of data in parallel in thousands of nodes in computer clusters results in gaining high performances. Open source cloud solutions such as Hadoop MapReduce, which is highly scalable and capable of processing large amount of data rapidly in parallel on large clusters provide better solution compared to RDBDM. Both GPUs and MapReduce are designed to support vast data parallelism. For performance considerations, GPU computing could be adopted over low performing CPU systems. This paper discusses MapReduce system using Hadoop and Mars. Mars is a MapReduce framework on graphics processor. Hence, the proposition is to use GPU based systems for earthquake simulations in which Digital elevation model 3D data sets are fully materialized where scientist can make use of these data for various analysis and simulations.

The requirement of semantic technology has been augmented day by day in IT related applications due to various features including interoperability. Effective development and organization of semantic knowledge are essential for maximum throughput in any application. We extend and develop two sub-domain computer resource ontologies and utilize them in a resource discovery process of a Grid computing environment to reduce the job rejection rate. Grid computing aggregates distributed computing resources to execute computationally complex jobs. The selection of resources in a Grid system involves finding and locating resources based on users' requirements. Identifying an appropriate resource selection mechanism for Grid jobs is a major concern because overall performance of a Grid depends on it and it also helps to schedule and allocate resources. We compute semantic similarity threshold values and employ both extended ontolgies in a decentralized resource discovery model of Grid Computing. The simulation is carried out using GridSim and PlanetSim to evaluate the effectiveness of a semantic resource discovery model. The results show improved success probability for complex jobs and reduce communication overheads compared to the non semantic resource discovery model.

The text of this chapter is based on []. Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data storage or network resources across dynamic and geographically dispersed organizations. The goal of grid task scheduling is to achieve high system throughput and to match the application needed with the available computing resources. This is matching of resources in a non-deterministically shared heterogeneous environment. The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. To obtain good methods to solve this problem a new area of research is implemented.

The coming of age of biological electron microscopy