ESPINA: a tool for the automated segmentation and counting of synapses in large stacks of electron microscopy images

The synapses in the cerebral cortex can be classified into two main types, Gray’s type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory) and symmetric (inhibitory GABAergic) synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze 3D samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using focused ion beam/scanning electron microscope microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed, and quantified from large 3D tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation, and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes.

Goal dependent vs goal independent analysis of logic programs

Goal independent analysis of logic programs is commonly discussed in the context of the bottom-up approach. However, while the literature is rich in descriptions of top-down analysers and their application, practical experience with bottom-up analysis is still in a preliminary stage. Moreover, the practical use of existing top-down frameworks for goal independent analysis has not been addressed in a practical system. We illustrate the efficient use of existing goal dependent, top-down frameworks for abstract interpretation in performing goal independent analyses of logic programs much the same as those usually derived from bottom-up frameworks. We present several optimizations for this flavour of top-down analysis. The approach is fully implemented within an existing top-down framework. Several implementation tradeoffs are discussed as well as the influence of domain characteristics. An experimental evaluation including a comparison with a bottom-up analysis for the domain Prop is presented. We conclude that the technique can offer advantages with respect to standard goal dependent analyses.

User-definable resource usage bounds analysis for java bytecode

Automatic cost analysis of programs has been traditionally concentrated on a reduced number of resources such as execution steps, time, or memory. However, the increasing relevance of analysis applications such as static debugging and/or certiflcation of user-level properties (including for mobile code) makes it interesting to develop analyses for resource notions that are actually application-dependent. This may include, for example, bytes sent or received by an application, number of files left open, number of SMSs sent or received, number of accesses to a datábase, money spent, energy consumption, etc. We present a fully automated analysis for inferring upper bounds on the usage that a Java bytecode program makes of a set of application programmer-deflnable resources. In our context, a resource is defined by programmer-provided annotations which state the basic consumption that certain program elements make of that resource. From these deflnitions our analysis derives functions which return an upper bound on the usage that the whole program (and individual blocks) make of that resource for any given set of input data sizes. The analysis proposed is independent of the particular resource. We also present some experimental results from a prototype implementation of the approach covering a signiflcant set of interesting resources.

Customizable resource usage analysis for java bytecode

Automatic cost analysis of programs has been traditionally studied in terms of a number of concrete, predefined resources such as execution steps, time, or memory. However, the increasing relevance of analysis applications such as static debugging and/or certification of user-level properties (including for mobile code) makes it interesting to develop analyses for resource notions that are actually applicationdependent. This may include, for example, bytes sent or received by an application, number of files left open, number of SMSs sent or received, number of accesses to a database, money spent, energy consumption, etc. We present a fully automated analysis for inferring upper bounds on the usage that a Java bytecode program makes of a set of application programmer-definable resources. In our context, a resource is defined by programmer-provided annotations which state the basic consumption that certain program elements make of that resource. From these definitions our analysis derives functions which return an upper bound on the usage that the whole program (and individual blocks) make of that resource for any given set of input data sizes. The analysis proposed is independent of the particular resource. We also present some experimental results from a prototype implementation of the approach covering an ample set of interesting resources.

Common motifs in scientific workflows: An empirical analysis

While workflow technology has gained momentum in the last decade as a means for specifying and enacting computational experiments in modern science, reusing and repurposing existing workflows to build new scientific experiments is still a daunting task. This is partly due to the difficulty that scientists experience when attempting to understand existing workflows, which contain several data preparation and adaptation steps in addition to the scientifically significant analysis steps. One way to tackle the understandability problem is through providing abstractions that give a high-level view of activities undertaken within workflows. As a first step towards abstractions, we report in this paper on the results of a manual analysis performed over a set of real-world scientific workflows from Taverna and Wings systems. Our analysis has resulted in a set of scientific workflow motifs that outline i) the kinds of data intensive activities that are observed in workflows (data oriented motifs), and ii) the different manners in which activities are implemented within workflows (workflow oriented motifs). These motifs can be useful to inform workflow designers on the good and bad practices for workflow development, to inform the design of automated tools for the generation of workflow abstractions, etc.

Common motifs in scientific workflows: An empirical analysis

Workflow technology continues to play an important role as a means for specifying and enacting computational experiments in modern science. Reusing and re-purposing workflows allow scientists to do new experiments faster, since the workflows capture useful expertise from others. As workflow libraries grow, scientists face the challenge of finding workflows appropriate for their task, understanding what each workflow does, and reusing relevant portions of a given workflow.We believe that workflows would be easier to understand and reuse if high-level views (abstractions) of their activities were available in workflow libraries. As a first step towards obtaining these abstractions, we report in this paper on the results of a manual analysis performed over a set of real-world scientific workflows from Taverna, Wings, Galaxy and Vistrails. Our analysis has resulted in a set of scientific workflow motifs that outline (i) the kinds of data-intensive activities that are observed in workflows (Data-Operation motifs), and (ii) the different manners in which activities are implemented within workflows (Workflow-Oriented motifs). These motifs are helpful to identify the functionality of the steps in a given workflow, to develop best practices for workflow design, and to develop approaches for automated generation of workflow abstractions.

Automated characterisation of intergrowth textures in mineral particles. A case study

The characterisation of mineral texture has been a major concern for process mineralogists, as liberation characteristics of the ores are intimately related to the mineralogical texture. While a great effort has been done to automatically characterise texture in unbroken ores, the characterisation of textural attributes in mineral particles is usually descriptive. However, the quantitative characterisation of texture in mineral particles is essential to improve and predict the performance of minerallurgical processes (i.e. all the processes involved in the liberation and separation of the mineral of interest) and to achieve a more accurate geometallurgical model. Driven by this necessity of achieving a more complete characterisation of textural attributes in mineral particles, a methodology has been recently developed to automatically characterise the type of intergrowth between mineral phases within particles by means of digital image analysis. In this methodology, a set ofminerallurgical indices has been developed to quantify different mineralogical features and to identify the intergrowth pattern by discriminant analysis. The paper shows the application of the methodology to the textural characterisation of chalcopyrite in the rougher concentrate of the Kansanshi copper mine (Zambia). In this sample, the variety of intergrowth patterns of chalcopyrite with the other minerals has been used to illustrate the methodology. The results obtained show that the method identifies the intergrowth type and provides quantitative information to achieve a complete and detailed mineralogical characterisation. Therefore, the use of this methodology as a routinely tool in automated mineralogy would contribute to a better understanding of the ore behaviour during liberation and separation processes.

New perspectives on quantitative textural analysis

Process mineralogy provides the mineralogical information required by geometallurgists to address the inherent variation of geological data. The successful benefitiation of ores mostly depends on the ability of mineral processing to be efficiently adapted to the ore characteristics, being liberation one of the most relevant mineralogical parameters. The liberation characteristics of ores are intimately related to mineral texture. Therefore, the characterization of liberation necessarily requieres the identification and quantification of those textural features with a major bearing on mineral liberation. From this point of view grain size, bonding between mineral grains and intergrowth types are considered as the most influential textural attributes. While the quantification of grain size is a usual output of automated current technologies, information about grain boundaries and intergrowth types is usually descriptive and difficult to quantify to be included in the geometallurgical model. Aiming at the systematic and quantitative analysis of the intergrowth type within mineral particles, a new methodology based on digital image analysis has been developed. In this work, the ability of this methodology to achieve a more complete characterization of liberation is explored by the analysis of chalcopyrite in the rougher concentrate of the Kansanshi copper-gold mine (Zambia). Results obtained show that the method provides valuable textural information to achieve a better understanding of mineral behaviour during concentration processes. The potential of this method is enhanced by the fact that it provides data unavailable by current technologies. This opens up new perspectives on the quantitative analysis of mineral processing performance based on textural attributes.

Probabilistic meta-analysis of risk from the exposure to Hg in artisanal gold mining communities in Colombia

Colombia is one of the largest per capita mercury polluters in the world as a consequence of its artisanal gold mining activities. The severity of this problem in terms of potential health effects was evaluated by means of a probabilistic risk assessment carried out in the twelve departments (or provinces) in Colombia with the largest gold production. The two exposure pathways included in the risk assessment were inhalation of elemental Hg vapors and ingestion of fish contaminated with methyl mercury. Exposure parameters for the adult population (especially rates of fish consumption) were obtained from nation-wide surveys and concentrations of Hg in air and of methyl-mercury in fish were gathered from previous scientific studies. Fish consumption varied between departments and ranged from 0 to 0.3 kg d?1. Average concentrations of total mercury in fish (70 data) ranged from 0.026 to 3.3 lg g?1. A total of 550 individual measurements of Hg in workshop air (ranging from menor queDL to 1 mg m?3) and 261 measurements of Hg in outdoor air (ranging from menor queDL to 0.652 mg m?3) were used to generate the probability distributions used as concentration terms in the calculation of risk. All but two of the distributions of Hazard Quotients (HQ) associated with ingestion of Hg-contaminated fish for the twelve regions evaluated presented median values higher than the threshold value of 1 and the 95th percentiles ranged from 4 to 90. In the case of exposure to Hg vapors, minimum values of HQ for the general population exceeded 1 in all the towns included in this study, and the HQs for miner-smelters burning the amalgam is two orders of magnitude higher, reaching values of 200 for the 95th percentile. Even acknowledging the conservative assumptions included in the risk assessment and the uncertainties associated with it, its results clearly reveal the exorbitant levels of risk endured not only by miner-smelters but also by the general population of artisanal gold mining communities in Colombia.

Study of PM10 and PM2.5 levels in three European cities: Analysis of intra and inter urban variations

In the present paper, 1-year PM10 and PM 2.5 data from roadside and urban background monitoring stations in Athens (Greece), Madrid (Spain) and London (UK) are analysed in relation to other air pollutants (NO,NO2,NOx,CO,O3 and SO2)and several meteorological parameters (wind velocity, temperature, relative humidity, precipitation, solar radiation and atmospheric pressure), in order to investigate the sources and factors affecting particulate pollution in large European cities. Principal component and regression analyses are therefore used to quantify the contribution of both combustion and non-combustion sources to the PM10 and PM 2.5 levels observed. The analysis reveals that the EU legislated PM 10 and PM2.5 limit values are frequently breached, forming a potential public health hazard in the areas studied. The seasonal variability patterns of particulates varies among cities and sites, with Athens and Madrid presenting higher PM10 concentrations during the warm period and suggesting the larger relative contribution of secondary and natural particles during hot and dry days. It is estimated that the contribution of non-combustion sources varies substantially among cities, sites and seasons and ranges between 38-67% and 40-62% in London, 26-50% and 20-62% in Athens, and 31-58% and 33-68% in Madrid, for both PM10 and PM 2.5. Higher contributions from non-combustion sources are found at urban background sites in all three cities, whereas in the traffic sites the seasonal differences are smaller. In addition, the non-combustion fraction of both particle metrics is higher during the warm season at all sites. On the whole, the analysis provides evidence of the substantial impact of non-combustion sources on local air quality in all three cities. While vehicular exhaust emissions carry a large part of the risk posed on human health by particle exposure, it is most likely that mitigation measures designed for their reduction will have a major effect only at traffic sites and additional measures will be necessary for the control of background levels. However, efforts in mitigation strategies should always focus on optimal health effects.

Application of a SPH depth-integrated model to landslide run-out analysis

Hazard and risk assessment of landslides with potentially long run-out is becoming more and more important. Numerical tools exploiting different constitutive models, initial data and numerical solution techniques are important for making the expert’s assessment more objective, even though they cannot substitute for the expert’s understanding of the site-specific conditions and the involved processes. This paper presents a depth-integrated model accounting for pore water pressure dissipation and applications both to real events and problems for which analytical solutions exist. The main ingredients are: (i) The mathematical model, which includes pore pressure dissipation as an additional equation. This makes possible to model flowslide problems with a high mobility at the beginning, the landslide mass coming to rest once pore water pressures dissipate. (ii) The rheological models describing basal friction: Bingham, frictional, Voellmy and cohesive-frictional viscous models. (iii) We have implemented simple erosion laws, providing a comparison between the approaches of Egashira, Hungr and Blanc. (iv) We propose a Lagrangian SPH model to discretize the equations, including pore water pressure information associated to the moving SPH nodes

Whole genome analysis: Experimental access to all genome sequenced segments through larger-scale efficient oligonucleotide synthesis and PCR

The recent ability to sequence whole genomes allows ready access to all genetic material. The approaches outlined here allow automated analysis of sequence for the synthesis of optimal primers in an automated multiplex oligonucleotide synthesizer (AMOS). The efficiency is such that all ORFs for an organism can be amplified by PCR. The resulting amplicons can be used directly in the construction of DNA arrays or can be cloned for a large variety of functional analyses. These tools allow a replacement of single-gene analysis with a highly efficient whole-genome analysis.

Yeast microarrays for genome wide parallel genetic and gene expression analysis

We have developed high-density DNA microarrays of yeast ORFs. These microarrays can monitor hybridization to ORFs for applications such as quantitative differential gene expression analysis and screening for sequence polymorphisms. Automated scripts retrieved sequence information from public databases to locate predicted ORFs and select appropriate primers for amplification. The primers were used to amplify yeast ORFs in 96-well plates, and the resulting products were arrayed using an automated micro arraying device. Arrays containing up to 2,479 yeast ORFs were printed on a single slide. The hybridization of fluorescently labeled samples to the array were detected and quantitated with a laser confocal scanning microscope. Applications of the microarrays are shown for genetic and gene expression analysis at the whole genome level.

Poly(rC) binding protein 2 binds to stem-loop IV of the poliovirus RNA 5' noncoding region: identification by automated liquid chromatography-tandem mass spectrometry.

The 5' noncoding region of poliovirus RNA contains an internal ribosome entry site (IRES) for cap-independent initiation of translation. Utilization of the IRES requires the participation of one or more cellular proteins that mediate events in the translation initiation reaction, but whose biochemical roles have not been defined. In this report, we identify a cellular RNA binding protein isolated from the ribosomal salt wash of uninfected HeLa cells that specifically binds to stem-loop IV, a domain located in the central part of the poliovirus IRES. The protein was isolated by specific RNA affinity chromatography, and 55% of its sequence was determined by automated liquid chromatography-tandem mass spectrometry. The sequence obtained matched that of poly(rC) binding protein 2 (PCBP2), previously identified as an RNA binding protein from human cells. PCBP2, as well as a related protein, PCBP1, was over-expressed in Escherichia coli after cloning the cDNAs into an expression plasmid to produce a histidine-tagged fusion protein. Specific interaction between recombinant PCBP2 and poliovirus stem-loop IV was demonstrated by RNA mobility shift analysis. The closely related PCBP1 showed no stable interaction with the RNA. Stem-loop IV RNA containing a three nucleotide insertion that abrogates translation activity and virus viability was unable to bind PCBP2.

Genetic analysis of type 1 diabetes using whole genome approaches.

Whole genome linkage analysis of type 1 diabetes using affected sib pair families and semi-automated genotyping and data capture procedures has shown how type 1 diabetes is inherited. A major proportion of clustering of the disease in families can be accounted for by sharing of alleles at susceptibility loci in the major histocompatibility complex on chromosome 6 (IDDM1) and at a minimum of 11 other loci on nine chromosomes. Primary etiological components of IDDM1, the HLA-DQB1 and -DRB1 class II immune response genes, and of IDDM2, the minisatellite repeat sequence in the 5' regulatory region of the insulin gene on chromosome 11p15, have been identified. Identification of the other loci will involve linkage disequilibrium mapping and sequencing of candidate genes in regions of linkage.