Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

A spontaneous mutation in MutL-Homolog 3 (HvMLH3) affects synapsis and crossover resolution in the barley desynaptic mutant des10

Although meiosis is evolutionarily conserved, many of the underlying mechanisms show species-specific differences. These are poorly understood in large genome plant species such as barley (Hordeum vulgare) where meiotic recombination is very heavily skewed to the ends of chromosomes. The characterization of mutant lines can help elucidate how recombination is controlled. We used a combination of genetic segregation analysis, cytogenetics, immunocytology and 3D imaging to genetically map and characterize the barley meiotic mutant DESYNAPTIC 10 (des10). We identified a spontaneous exonic deletion in the orthologue of MutL-Homolog 3 (HvMlh3) as the causal lesion. Compared with wild-type, des10 mutants exhibit reduced recombination and fewer chiasmata, resulting in the loss of obligate crossovers and leading to chromosome mis-segregation. Using 3D structured illumination microscopy (3D-SIM), we observed that normal synapsis progression was also disrupted in des10, a phenotype that was not evident with standard confocal microscopy and that has not been reported with Mlh3 knockout mutants in Arabidopsis. Our data provide new insights on the interplay between synapsis and recombination in barley and highlight the need for detailed studies of meiosis in nonmodel species. This study also confirms the importance of early stages of prophase I for the control of recombination in large genome cereals.Isabelle Colas, Malcolm Macaulay, James D. Higgins, Dylan Phillips, Abdellah Barakate ... Robbie Waugh ... et al

Spatial domaining of highly variable continuous geometallurgical data

An outcome of geometallurgical mapping and modeling is the generation of continuous down hole profiles of quantitative geometallurgical attributes at assay scale. These attributes vary from traditional routine data collection methods such as assays and geotechnical logging (e.g. RQD, Fracture Frequency), to new measurements including petrophysical attributes, EQUOtip hardness data and modelled estimates of metallurgical performance indices (e.g. A *b, BMWi). Highly variable continuous geometallurgical data with high spatial resolution can make it difficult to identify spatially continuous domains. An automated method based on the well known time series analysis technique of cumulative summation (CuSum) has been developed which uses statistical analysis to identify domain boundaries in down hole profiles. A bootstrap analysis automatically identifies potential domain boundary locations with associated confidence levels and a series of t-tests determine the significance of defined populations. A hierarchical clustering algorithm is applied to results to enable data sensitivity to be taken into account. The method produces statistically significant populations and is suitable for application on either single or multiple drill hole datasets. Domains defined preserve the inherent variability of the continuous down hole data and provide a method for scaling up variability data (i.e. assay interval scale) to mine scale (i.e. bench scale, block model). A major advantage of the method is its ability to provide suitable input parameters for use in spatial modeling of non-additive and non-linear geometallurgical attributes through conditional simulation techniques

Geometallurgical mapping and modelling of comminution performance at the Cadia East porphyry deposit

Cu-Au porphyry deposits are large tonnage low-grade resources dependent on processing large volumes of material. The ability to understand and predict comminution perfonnance is critical, as it is frequently the rate-limiting step during processing. Parameters required from geometallurgical mapping and modelling of comminution perfonnance are: • Axb processing domains based on impact breakage variability, and • BMWi processing domains showing grind response variability which lead to • Mine throughput domains for a specific comminution circuit configuration. Due to the cost and/or large sample requirements of traditional comminution tests, insufficient data density and distribution of comminution indices commonly exist, making it difficult to model and map deposit scale variability. One method routinely applied to three dimensional domaining is to have an a priori zoning based on geological features (lithology, alteration, mineralogy, textures), and then apply to all material within a given domain the metallurgical values measured on a single sample or average of samples from that domain. The problem with this approach is that commonly the geological domains do not reflect comminution response, therefore compromising the quality of the average domain estimate. To resolve this problem an alternate integrated geometallurgical mapping and modelling method is applied to model and map comminution performance at Cadia East. This paperuses adata set of -32000 assay measurements andgeologicallogginginformation, integrated with -150 comminution measurements to analyse and develop proxy support models for comminution indices (ie Axb, BMWi, and throughput) based on inherent geological variability. Discrete downhole comminution processing domains are created, mapping orebody response while maintaining inherent variability

An integrated approach of predicting metallurgical performance relating to variability in deposit characteristics

An integrated approach that is applicable to existing operations and early stage of mining project such as prefeasibility and feasibility studies is developed to asses deposit variability and its influence on metallurgical performance. The approach is based on linking the techniques of geometallurgy and circuit simulation. It allows better understanding of deposit variability, effective selection of drill core samples from the deposit, quick and efficient prediction of metallurgical performance, and provides more knowledge to a holistic approach of circuit and tailings design and optimisation in prefeasibility/feasibility stage. A case study is performed on Finney's Hill to demonstrate the approach. (C) 2014 Elsevier Ltd. All rights reserved

Isolating the impact of rock properties and operational settings on minerals processing performance: A data-driven approach

Mining operations record a large amount of data from multiple sources (such as block model and online processing data) which is neither effectively nor systematically used to understand and improve operational performance. This paper proposes a generic semi-automatable data analytics method, the Integrated Analysis Method (IAM), that addresses the disconnection between disparate datasets. IAM enables evidence-based understanding of rock and machine parameters, laying the foundation for a potentially more sophisticated way to model and predict mining processes to deliver financial value. IAM systematically combines and analyses both rock characteristics and operational data to isolate the impact of the variability in rock characteristics and operational settings on key performances. Insights extracted from IAM allow one to narrow down key operating conditions, specific to a particular plant, that are correlated to, for example, significant differences in daily throughput while processing batches of ore with similar metallurgical characteristics. Such insights can be used for multiple purposes, for instance, to learn optimal processing recipes for a given set of rock properties. We applied IAM to a combined data set recorded at a Chilean ore deposit and evaluated our findings with domain experts

Expression of N‐cycling genes of root microbiomes provides insights for sustaining oilseed crop production

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