Classification of the Johnson Space Center stratospheric dust collection

A review of 291 catalogued particles on the bases of particle size, shape, bulk chemistry, and texture is used to establish a reliable taxonomy. Extraterrestrial materials occur in three defined categories: spheres, aggregates and fragments. Approximately 76% of aggregates are of probable extraterrestrial origin, whereas spheres contain the smallest amount of extraterrestrial material (approx 43%). -B.M.

Supplementary Information : Hogan, Holland, Holloway, Petit and Read : Read Classification for Next Generation Sequencing, ESANN 2013, April 2013

This item provides supplementary materials for the paper mentioned in the title, specifically a range of organisms used in the study. The full abstract for the main paper is as follows: Next Generation Sequencing (NGS) technologies have revolutionised molecular biology, allowing clinical sequencing to become a matter of routine. NGS data sets consist of short sequence reads obtained from the machine, given context and meaning through downstream assembly and annotation. For these techniques to operate successfully, the collected reads must be consistent with the assumed species or species group, and not corrupted in some way. The common bacterium Staphylococcus aureus may cause severe and life-threatening infections in humans,with some strains exhibiting antibiotic resistance. In this paper, we apply an SVM classifier to the important problem of distinguishing S. aureus sequencing projects from alternative pathogens, including closely related Staphylococci. Using a sequence k-mer representation, we achieve precision and recall above 95%, implicating features with important functional associations.

Effects of rice husk biochar and sugar-mill by-products on methane consumption from two different soils

Methane (CH4) is an important greenhouse gas with a global warming potential (GWP) 25 times greater than carbon dioxide (CO2) that can be produced or consumed in soils depending on environmental conditions and other factors. Biochar application to soils has been shown to reduce CH4 emissions and to increase CH4 consumption. However, the effects of rice husk biochar (RB) have not been thoroughly investigated. Two 60-day laboratory incubation experiments were conducted to investigate the effects of amending two soil types with RB, raw mill mud (MM) and composted mill mud (CM) on soil CH4 consumption and emissions. Soil cores incubated in 1 L glass jars and gas samples were analysed for CH4 using gas chromatography. Average CH4 consumption rates varied from -0.06 to -0.68 g CH4-C( )/ha/d in sandy loam soil and -0.59 to -1.00 g CH4-C/ha/d in clay soil. Application of RB resulted in CH4 uptake of -0.52 to -0.55 g CH4-C/ha/d in sandy loam and -0.76 to -0.91 g CH4-C/ha/d in clay soil. Addition of MM showed low CH4 emissions or consumption at 60% water-filled pore space (WFPS) in both soils. However, at high water contents (>75% WFPS) the application of MM produced high rates of CH4 emissions which were significantly suppressed when RB was added. Cumulative emissions of the MM treatment produced 108.9 g CH4-C/ha at 75% WFPS and 11 459.3 g CH4-C/ha at 90% WFPS in sandy loam soil over a period of 60 days. RB can increase CH4 uptake under low soil water content (SWC) and decrease CH4 emissions under anaerobic conditions. CM expressed more potential to reduce CH4 emissions than those of MM.

Classification of railway bridges based on criticality and vulnerability factors

Bridges are currently rated individually for maintenance and repair action according to the structural conditions of their elements. Dealing with thousands of bridges and the many factors that cause deterioration, makes this rating process extremely complicated. The current simplified but practical methods are not accurate enough. On the other hand, the sophisticated, more accurate methods are only used for a single or particular bridge type. It is therefore necessary to develop a practical and accurate rating system for a network of bridges. The first most important step in achieving this aim is to classify bridges based on the differences in nature and the unique characteristics of the critical factors and the relationship between them, for a network of bridges. Critical factors and vulnerable elements will be identified and placed in different categories. This classification method will be used to develop a new practical rating method for a network of railway bridges based on criticality and vulnerability analysis. This rating system will be more accurate and economical as well as improve the safety and serviceability of railway bridges.

Refinements and developments on the stratospheric dust database and classification scheme

Greater than 750 individual particles have now been selected from collection flags housed in the JSC Cosmic Dust Curatorial Facility and most have been documented in the Cosmic Dust Catalogs [1]. As increasing numbers of particles are placed in Cosmic Dust Collections, and a greater diversity of particles are introduced to the stratosphere through natural and man-made processes (e.g. decaying orbits of space debris [2]), there is an even greater need for a classification scheme to encompass all stratospheric particles rather than only extraterrestrial particles. The fundamental requirements for a suitable classification scheme have been outlined in earlier communications [3,4]. A quantitative survey of particles on collection flag W7017 indicates that there is some bias in the number of samples selected within a given category for the Cosmic Dust Catalog [5]. However, the sample diversity within this selection is still appropriate for the development of a reliable classification scheme. In this paper, we extend the earlier works on stratospheric particle classification to include particles collected during the period May 1981 to November 1983.

Increasing NH4+ retention efficiency of sandy soils by adding a modified kaolin material: Mesolite

Water and ammonium retention by sandy soils may be low and result in leaching of applied fertiliser. To increase water and nutrient retention, zeolite is sometimes applied as a soil ameliorant for high value land uses including turf and horticulture. We have used a new modified kaolin material (MesoLite) as a soil amendment to test the efficiency of NH4+ retention and compared the results with natural zeolite. MesoLite is made by caustic reaction of kaolin at temperature between 80-95°C; although it has a moderate surface area, its cation exchange capacity is very high;(SA=13m2/g,CEC=500meq/100g). A 13cm tall sand column filled with ~450g of sandy soil homogeneously mixed with 1, 2, 4, and 8g of MesoLite or natural zeolite per 1kg of soil was prepared. After saturation with local bore water, concentrated ammonium sulfate solution was injected at the base. Then, bore water was passed from bottom to top through the column at amounts up to 6 pore volumes and at a constant flow rate of 10ml/min using a peristaltic pump. Concentrations of leached NH4+ were determined using an AutoAnalyser. The concentration of NH4+ leached from the column with 0.4% MesoLite was greatly (90%) reduced relative to unamended soil. Under these conditions NH4+ retention by the soil-MesoLite mixture was 11.5 times more efficient than the equivalent soil-natural zeolite mixture. Glasshouse experiments conducted in a separate study show that NH4+ adsorbed by MesoLite is available to plants.

Read classification for next generation sequencing

Next Generation Sequencing (NGS) has revolutionised molec- ular biology, allowing routine clinical sequencing. NGS data consists of short sequence reads, given context through downstream assembly and annotation, a process requiring reads consistent with the assumed species or species group. The common bacterium Staphylococcus aureus may cause severe and life-threatening infections in humans, with some strains exhibiting antibiotic resistance. Here we apply an SVM classifier to the important problem of distinguishing S. aureus sequencing projects from other pathogens, including closely related Staphylococci. Using a sequence k-mer representation, we achieve precision and recall above 95%, implicating features with important functional associations.

Cardiomyopathy classification : ongoing debate in the genomics era

Cardiomyopathies represent a group of diseases of the myocardium of the heart and include diseases both primarily of the cardiac muscle and systemic diseases leading to adverse effects on the heart muscle size, shape, and function. Traditionally cardiomyopathies were defined according to phenotypical appearance. Now, as our understanding of the pathophysiology of the different entities classified under each of the different phenotypes improves and our knowledge of the molecular and genetic basis for these entities progresses, the traditional classifications seem oversimplistic and do not reflect current understanding of this myriad of diseases and disease processes. Although our knowledge of the exact basis of many of the disease processes of cardiomyopathies is still in its infancy, it is important to have a classification system that has the ability to incorporate the coming tide of molecular and genetic information. This paper discusses how the traditional classification of cardiomyopathies based on morphology has evolved due to rapid advances in our understanding of the genetic and molecular basis for many of these clinical entities.

Iron nodules in ferric soils of the Fraser Coast, Australia : relicts of laterisation or features of contemporary weathering and pedogenesis?

The genesis of ferruginous nodules and pisoliths in soils and weathering profiles of coastal southern and eastern Australia has long been debated. It is not clear whether iron (Fe) nodules are redox accumulations, residues of Miocene laterite duricrust, or the products of contemporary weathering of Fe-rich sedimentary rocks. This study combines a catchment-wide survey of Fe nodule distribution in Poona Creek catchment (Fraser Coast, Queensland) with detailed investigations of a representative ferric soil profile to show that Fe nodules are derived from Fe-rich sandstones. Where these crop out, they are broken down, transported downslope by colluvial processes, and redeposited. Chemical and physical weathering transforms these eroded rock fragments into non-magnetic Fe nodules. Major features of this transformation include lower hematite/goethite and kaolinite/gibbsite ratios, increased porosity, etching of quartz grains, and development of rounded morphology and a smooth outer cortex. Iron nodules are commonly concentrated in ferric horizons. We show that these horizons form as the result of differential biological mixing of the soil. Bioturbation gradually buries nodules and rock fragments deposited at the surface of the soil, resulting in a largely nodule-free 'biomantle' over a ferric 'stone line'. Maghemite-rich magnetic nodules are a prominent feature of the upper half of the profile. These are most likely formed by the thermal alteration of non-magnetic nodules located at the top of the profile during severe bushfires. They are subsequently redistributed through the soil profile by bioturbation. Iron nodules occurring in the study area are products of contemporary weathering of Fe-rich rock units. They are not laterite duricrust residues nor are they redox accumulations, although redox-controlled dissolution/re-precipitation is an important component of post-depositional modification of these Fe nodules.

Laboratory measurement of hydraulic conductivity functions of two unsaturated sandy soils during drying and wetting processes

The importance of applying unsaturated soil mechanics to geotechnical engineering design has been well understood. However, the consumption of time and the necessity for a specific laboratory testing apparatus when measuring unsaturated soil properties have limited the application of unsaturated soil mechanics theories in practice. Although methods for predicting unsaturated soil properties have been developed, the verification of these methods for a wide range of soil types is required in order to increase the confidence of practicing engineers in using these methods. In this study, a new permeameter was developed to measure the hydraulic conductivity of unsaturated soils using the steady-state method and directly measured suction (negative pore-water pressure) values. The apparatus is instrumented with two tensiometers for the direct measurement of suction during the tests. The apparatus can be used to obtain the hydraulic conductivity function of sandy soil over a low suction range (0-10 kPa). Firstly, the repeatability of the unsaturated hydraulic conductivity measurement, using the new permeameter, was verified by conducting tests on two identical sandy soil specimens and obtaining similar results. The hydraulic conductivity functions of the two sandy soils were then measured during the drying and wetting processes of the soils. A significant hysteresis was observed when the hydraulic conductivity was plotted against the suction. However, the hysteresis effects were not apparent when the conductivity was plotted against the volumetric water content. Furthermore, the measured unsaturated hydraulic conductivity functions were compared with predictions using three different predictive methods that are widely incorporated into numerical software. The results suggest that these predictive methods are capable of capturing the measured behavior with reasonable agreement.

Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms

Highly sensitive infrared cameras can produce high-resolution diagnostic images of the temperature and vascular changes of breasts. Wavelet transform based features are suitable in extracting the texture difference information of these images due to their scale-space decomposition. The objective of this study is to investigate the potential of extracted features in differentiating between breast lesions by comparing the two corresponding pectoral regions of two breast thermograms. The pectoral regions of breastsare important because near 50% of all breast cancer is located in this region. In this study, the pectoral region of the left breast is selected. Then the corresponding pectoral region of the right breast is identified. Texture features based on the first and the second sets of statistics are extracted from wavelet decomposed images of the pectoral regions of two breast thermograms. Principal component analysis is used to reduce dimension and an Adaboost classifier to evaluate classification performance. A number of different wavelet features are compared and it is shown that complex non-separable 2D discrete wavelet transform features perform better than their real separable counterparts.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.