A PR-quadtree based multi-dimensional indexing for complex query in a cloud system

The state-of-the-art indexing mechanisms for distributed cloud data management systems can not support complex queries, such as multi-dimensional query and range query. To solve this problem, we propose a multi-dimensional indexing mechanism named PR-Chord to support complex queries. PR-Chord is composed of the global index named PR-Index and the Chord network. The multi-dimensional space formed by the range of the multi-dimensional data is divided into hyper-rectangle spaces equally. The PR-Index is a hierarchical index structure based on the improved PR quadtree to index these spaces. The complex query is transformed into the query of leaf nodes of PR-Index. We design the algorithms of query, insertion and deletion to support complex queries. Since PR-Index does not store the multi-dimensional data, its maintenance cost is zero. PR-Chord has the advantages of load balancing and simple algorithm. The experiment results demonstrate that PR-Chord has good query efficiency.     

Malleable applications for scalable high performance computing

Iterative applications are known to run as slow as their slowest computational component. This paper introduces malleability, a new dynamic reconfiguration strategy to overcome this limitation. Malleability is the ability to dynamically change the data size and number of computational entities in an application. Malleability can be used by middleware to autonomously reconfigure an application in response to dynamic changes in resource availability in an architecture-aware manner, allowing applications to optimize the use of multiple processors and diverse memory hierarchies in heterogeneous environments. The modular Internet Operating System (IOS) was extended to reconfigure applications autonomously using malleability. Two different iterative applications were made malleable. The first is used in astronomical modeling, and representative of maximum-likelihood applications was made malleable in the SALSA programming language. The second models the diffusion of heat over a two dimensional object, and is representative of applications such as partial differential equations and some types of distributed simulations. Versions of the heat application were made malleable both in SALSA and MPI. Algorithms for concurrent data redistribution are given for each type of application. Results show that using malleability for reconfiguration is 10 to 100 times faster on the tested environments. The algorithms are also shown to be highly scalable with respect to the quantity of data involved. While previous work has shown the utility of dynamically reconfigurable applications using only computational component migration, malleability is shown to provide up to a 15% speedup over component migration alone on a dynamic cluster environment. This work is part of an ongoing research effort to enable applications to be highly reconfigurable and autonomously modifiable by middleware in order to efficiently utilize distributed environments. Grid computing environments are becoming increasingly heterogeneous and dynamic, placing new demands on applications’ adaptive behavior. This work shows that malleability is a key aspect in enabling effective dynamic reconfiguration of iterative applications in these environments.

Mitochondrial swelling measurement in situ by optimized spatial filtering: astrocyte-neuron differences

Mitochondrial swelling is a hallmark of mitochondrial dysfunction, and is an indicator of the opening of the mitochondrial permeability transition pore. We introduce here a novel quantitative in situ single-cell assay of mitochondrial swelling based on standard wide-field or confocal fluorescence microscopy. This morphometric technique quantifies the relative diameter of mitochondria labeled by targeted fluorescent proteins. Fluorescence micrographs are spatial bandpass filtered transmitting either high or low spatial frequencies. Mitochondrial swelling is measured by the fluorescence intensity ratio of the high- to low-frequency filtered copy of the same image. We have termed this fraction the “thinness ratio”. The filters are designed by numeric optimization for sensitivity. We characterized the thinness ratio technique by modeling microscopic image formation and by experimentation in cultured cortical neurons and astrocytes. The frequency domain image processing endows robustness and subresolution sensitivity to the thinness ratio technique, overcoming the limitations of shape measurement approaches. The thinness ratio proved to be highly sensitive to mitochondrial swelling, but insensitive to fission or fusion of mitochondria. We found that in situ astrocytic mitochondria swell upon short-term uncoupling or inhibition of oxidative phosphorylation, whereas such responses are absent in cultured cortical neurons.     

An optimized procedure for whole-mount in situ hybridization on mouse embryos and embryoid bodies

Determining the precise expression pattern of a gene of interest at various stages of development is essential to understanding its biological function in embryology. This protocol describes a sensitive method for whole-mount in situ hybridization (WISH) to mouse embryos, using cRNA probes. Adaptations are provided that allow the protocol to be applied to embryonic stages ranging from blastocysts to postimplantation stage embryos, and to embryoid bodies. We also describe an in situ method for differential detection of two probes. Probe labeling and dissection and preparation of the embryos can be performed in 2 d. The actual WISH procedure can be completed in another 3 d.     

Simulative performance analysis of gossip failure detection for scalable distributed systems

Three protocols for gossip-based failure detection services in large-scale heterogeneous clusters are analyzed and compared. The basic gossip protocol provides a means by which failures can be detected in large distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. The hierarchical protocol leverages the underlying network topology to achieve faster failure detection. In addition to studying the effectiveness and efficiency of these two agreement protocols, we propose a third protocol that extends the hierarchical approach by piggybacking gossip information on application-generated messages. The protocols are simulated and evaluated with a fault-injection model for scalable distributed systems comprised of clusters of workstations connected by high-performance networks, such as the CPlant system at Sandia National Laboratories. The model supports permanent and transient node and link failures, with rates specified at simulation time, for processors functioning in a fail-silent fashion. Through high-fidelity, CAD-based modeling and simulation, we demonstrate the strengths and weaknesses of each approach in terms of agreement time, number of gossips, and overall scalability. This revised version was published online in July 2006 with corrections to the Cover Date.     

Comparison of membrane- and cloth-cathode assembly for scalable microbial fuel cells: Construction,performance and cost

The aim of this study is to compare the performance of different membrane cathode assembly (MCA) and cloth-cathode assembly (CCA) in air-chamber microbial fuel cells (MFCs) and provide an optimum cathode configuration for MFC scaling up. Two MCAs were prepared by hot-pressing carbon cloth containing cathodic catalyst to anion exchange membrane (AEM) and cation exchange membrane (CEM), respectively. A CCA was built by coating GORE-TEX^® cloth with a mixture of nickel-based conductive paint and cathodic catalyst. Under the fed-batch mode using brewery wastewater, the MFCs were compared with respect to power production, coulombic efficiency, COD removal, internal resistance and material cost. The experimental results show that CCA is a more favorable alternative than MCAs due to its easier preparation, higher maximum power density and COD removal, and lower internal resistance and cost. The optimum cathode assembly of CCA is cost-effective and mechanically robust enough to meet the important requirements for MFC scalability.     

Windex: a toolset for indexing helices

We describe here a set of procedures and algorithms that may be used as an aid in determining the indexing rule of a helical specimen. Crystallizing macromolecules into helical arrays has the potential to speed up and simplify the process of three-dimensional reconstruction of the macromolecular structure. The process of helical reconstruction has been largely automated except for the critical first step of indexing the helical diffraction pattern. This is quite often the rate-limiting step in the overall process, particularly in the case of large helical tubes, which have complicated helical diffraction patterns that may vary from tube to tube. We have developed a set of procedures, supported by a graphical user interface, that provide a straightforward and semi-automated approach to indexing a helical structure. The new procedures have been tested using a number of helical specimens, including TMV, acto-myosin, decorated microtubules, and a variety of helical tubes of a bacterial membrane protein.     

An optimized protocol for large‐scale in situ sampling and analysis of volatile organic compounds

Chemical ecology is an ever‐expanding field with a growing interest in population‐ and community‐level studies. Many such studies are hindered due to lack of an efficient and accelerated protocol for large‐scale sampling and analysis of chemical compounds. Here, we present an optimized protocol for such large‐scale study of volatiles. A large‐scale in situ study to understand role of semiochemicals in variation in mating success of lekking blackbuck was conducted. Suitable methods for sampling and statistical analysis were identified by testing and comparing the efficiencies of available techniques to reduce analysis time while retaining sensitivity and comprehensiveness. Solid‐phase extraction using polydimethylsiloxane, analysis using a semiautomated detection of retention time and base peak, and statistical analysis using random forest algorithm were identified as the most efficient methods for large‐scale in situ sampling and analysis of volatiles. The protocol for large‐scale volatile analysis can facilitate evolutionary and metaecological studies of volatiles in situ from all types of biological samples. The protocol has potential for wider application with the analysis and interpretation methods being suitable for all kinds of semiochemicals, including nonvolatile chemicals.     

A 24-microwell plate with improved mixing and scalable performance for high throughput cell cultures

Multi-well plates are widely used in high throughput drug screening, cell clone development, media design and cell culture optimization in the biotechnology industry. The reproducibility and data quality of cell cultures in multi-well plates are greatly affected by mixing, aeration, and evaporation. A novel 24-microwell plate (MWP) with static mixers for improved mixing and aeration, and gas permeable lids for reduced evaporation was developed for cell cultures. Mixing, oxygen transfer, evaporation, and cell proliferation as affected by the static mixer, shape of the well and agitation rate were studied. The static mixer improved mixing pattern and reduced cell aggregation under orbital shaking conditions. Consequently, the static mixer also improved cell proliferation with a significantly higher specific growth rate in round wells. In general, consistent growth kinetics was observed for cells cultured on the plate. Overall, the MWP improved the data quality with smaller standard deviations and better reproducibility. Furthermore, CHO cells cultured in the MWP gave similar kinetics in glucose consumption, lactate production, cell growth and viability, and antibody production in a serum-free medium to those cultured in spinner flasks, demonstrating its scalable performance and potential application in high throughput screening for cell culture process development.     

Nucleic-acid-based methods for monitoring the performance of in situ bioremediation

Over the last decade, major advancements have occurred in the application of nucleic-acid-based methods to detect and determine the levels of catabolic genes in environmental samples. Studies have focused on validating methods in microcosms, studying changes in the structure and expression of microbial communities in response to contaminants, and improving the sensitivity of the methods. Only in the last few years have these methods transitioned from development and validation to efforts to apply these methods for monitoring in situ bioremediation. Methods that analyse nucleic acids extracted from environmental samples are of value to bioremediation because they allow analysis independent of the artefacts that can arise from laboratory biodegradative potential assays and laboratory culture-based enumerations and from the inability to culture a large proportion of the micro-organisms in the environment In theory, these methods enable a more comprehensive perspective, and a more defensible interpretation, of the microbial community response to intrinsic and engineered bioremediation processes. Results from the first studies applying nucleic-acid-based methods to intrinsic or engineered bioremediation indicate that these methods have both potential and limitations. The rapidly increasing number of cloned and sequenced catabolic genes, methodological advancements such as the ability to track specific micro-organisms without prior sequence data, and the potential use of bioaugmentation in the field suggest that the utility of these methods for in situ bioremediation will increase in the coming years.     

PSI: indexing protein structures for fast similarity search

MOTIVATION: We consider the problem of finding similarities in protein structure databases. Current techniques sequentially compare the given query protein to all of the proteins in the database to find similarities. Therefore, the cost of similarity queries increases linearly as the volume of the protein databases increase. As the sizes of experimentally determined and theoretically estimated protein structure databases grow, there is a need for scalable searching techniques. RESULTS: Our techniques extract feature vectors on triplets of SSEs (Secondary Structure Elements). Later, these feature vectors are indexed using a multidimensional index structure. For a given query protein, this index structure is used to quickly prune away unpromising proteins in the database. The remaining proteins are then aligned using a popular alignment tool such as VAST. We also develop a novel statistical model to estimate the goodness of a match using the SSEs. Experimental results show that our techniques improve the pruning time of VAST 3 to 3.5 times while maintaining similar sensitivity.     

Biomass measurement online: the performance of in situ measurements and software sensors

Biomass measurement is one of the most critical measurements in biotechnological processes. The technologies developed for the measurement of biomass in situ have developed over the years. Because it has been over 10 years since the last review concentrating on practical issues concerning biomass measurements, it is time to evaluate recent developments in the field. This review concentrates on the applications of dielectric spectroscopy, optical density, infrared spectroscopy, and fluorescence for in situ measurement of biomass. The advantages offered by these methods and an economic way of estimating biomass concentration, the software sensors, are considered.     

DataStager: scalable data staging services for petascale applications

Known challenges for petascale machines are that (1) the costs of I/O for high performance applications can be substantial, especially for output tasks like checkpointing, and (2) noise from I/O actions can inject undesirable delays into the runtimes of such codes on individual compute nodes. This paper introduces the flexible ‘DataStager’ framework for data staging and alternative services within that jointly address (1) and (2). Data staging services moving output data from compute nodes to staging or I/O nodes prior to storage are used to reduce I/O overheads on applications’ total processing times, and explicit management of data staging offers reduced perturbation when extracting output data from a petascale machine’s compute partition. Experimental evaluations of DataStager on the Cray XT machine at Oak Ridge National Laboratory establish both the necessity of intelligent data staging and the high performance of our approach, using the GTC fusion modeling code and benchmarks running on 1000+ processors.     

2SNP: scalable phasing method for trios and unrelated individuals

Emerging microarray technologies allow affordable typing of very long genome sequences. A key challenge in analyzing of such huge amount of data is scalable and accurate computational inferring of haplotypes (i.e., splitting of each genotype into a pair of corresponding haplotypes). In this paper, we first phase genotypes consisting only of two SNPs using genotypes frequencies adjusted to the random mating model and then extend phasing of two-SNP genotypes to phasing of complete genotypes using maximum spanning trees. Runtime of the proposed 2SNP algorithm is O(nm (n + log m), where n and m are the numbers of genotypes and SNPs, respectively, and it can handle genotypes spanning entire chromosomes in a matter of hours.On datasets across 23 chromosomal regions from HapMap[11], 2SNP is several orders of magnitude faster than GERBIL and PHASE while matching them in quality measured by the number of correctly phased genotypes, single-site and switching errors. For example the 2SNP software phases entire chromosome (10(5) SNPs from HapMap) for 30 individuals in 2 hours with average switching error 7.7%.We have also enhanced 2SNP algorithm to phase family trio data and compared it with four other well-known phasing methods on simulated data from [15]. 2SNP is much faster than all of them while loosing in quality only to PHASE. 2SNP software is publicly available at http://alla.cs.gsu.edu/~software/2SNP.     

VolPy: Automated and scalable analysis pipelines for voltage imaging datasets

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy’s performance in spike extraction and scalability are state-of-the-art.     

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.     

Seqcrawler: biological data indexing and browsing platform

ABSTRACT: BACKGROUND: Seqcrawler takes its roots in software like SRS or Lucegene. It provides an indexing platform to ease the search of data and meta-data in biological banks and it can scale to face the current flow of data. While many biological bank search tools are available on the Internet, mainly provided by large organizations to search in their data, there is a lack of free and open source solution to browse one own set of data with a flexible query system and able to scale from single computer to a cloud system. A personal index platform will help labs and bioinformaticians to search in their meta-data but also to build a larger information system with custom subsets of data. RESULTS: The software is scalable from a single computer to a cloud-based infrastructure. It has been successfully tested in a private cloud with 3 index shards (piece of index) hosting ~400 millions of sequence information (whole GenBank, UniProt, PDB and others) for a total size of 600 GB in a fault tolerant architecture (high-availability). It has also been successfully integrated with software to add extra meta-data from blast results to enhance user's result analysis. CONCLUSIONS: Seqcrawler provides a complete open source search and store solution for labs or platforms needing to manage large amount of data/meta-data with a flexible and customizable web interface. All components (search engine, visualization and data storage), though independent, share a common and coherent data system that can be queried with a simple HTTP interface. The solution scales easily and can also provide a high availability infrastructure.     

An optimized method for in situ hybridization with signal amplification that allows the detection of rare mRNAs.

In situ hybridization (ISH) using nonradioactive probes enables mRNAs to be detected with improved cell resolution but compromised sensitivity compared to ISH with radiolabeled probes. To detect rare mRNAs, we optimized several parameters for ISH using digoxygenin (DIG)-labeled probes, and adapted tyramide signal amplification (TSA) in combination with alkaline phosphatase (AP)-based visualization. This method, which we term TSA-AP, achieves the high sensitivity normally associated with radioactive probes but with the cell resolution of chromogenic ISH. Unlike published protocols, long RNA probes (up to 2.61 kb) readily permeated cryosections and yielded stronger hybridization signals than hydrolyzed probes of equivalent complexity. RNase digestion after hybridization was unnecessary and led to a substantial loss of signal intensity without significantly reducing nonspecific background. Probe concentration was also a key parameter for improving signal-to-noise ratio in ISH. Using these optimized methods on rat taste tissue, we detected mRNA for mGluR4, a receptor, and transducin, a G-protein, both of which are expressed at very low abundance and are believed to be involved in chemosensory transduction. Because the effect of the tested parameters was similar for ISH on sections of brain and tongue, we believe that these methodological improvements for detecting rare mRNAs may be broadly applicable to other tissues. (J Histochem Cytochem 47:431-445, 1999)