Genome-wide Purification of Extrachromosomal Circular DNA from Eukaryotic Cells

Extrachromosomal circular DNAs (eccDNAs) are common genetic elements in Saccharomyces cerevisiae and are reported in other eukaryotes as well. EccDNAs contribute to genetic variation among somatic cells in multicellular organisms and to evolution of unicellular eukaryotes. Sensitive methods for detecting eccDNA are needed to clarify how these elements affect genome stability and how environmental and biological factors induce their formation in eukaryotic cells. This video presents a sensitive eccDNA-purification method called Circle-Seq. The method encompasses column purification of circular DNA, removal of remaining linear chromosomal DNA, rolling-circle amplification of eccDNA, deep sequencing, and mapping. Extensive exonuclease treatment was required for sufficient linear chromosomal DNA degradation. The rolling-circle amplification step by φ29 polymerase enriched for circular DNA over linear DNA. Validation of the Circle-Seq method on three S. cerevisiae CEN.PK populations of 1010 cells detected hundreds of eccDNA profiles in sizes larger than 1 kilobase. Repeated findings of ASP3-1, COS111, CUP1, RSC30, HXT6, HXT7 genes on circular DNA in both S288c and CEN.PK suggests that DNA circularization is conserved between strains at these loci. In sum, the Circle-Seq method has broad applicability for genome-scale screening for eccDNA in eukaryotes as well as for detecting specific eccDNA types

Detecting behavioral conflicts among crosscutting concerns

Aspects have been successfully promoted as a means to improve the modularization of software in the presence of crosscutting concerns. Within the Ideals project, aspects have been shown to be valuable for improving the modularization of idioms (see also Chapter 1). The so-called aspect interference problem is considered to be one of the remaining challenges of aspect-oriented software development: aspects may interfere with the behavior of the base code or other aspects. Especially interference among aspects is difficult to prevent, as this may be caused solely by the composition of aspects that behave correctly in isolation. A typical situation where this may occur is when multiple advices are applied at the same, or shared, join point. In this chapter we explain the problem of behavioral conflicts among aspects at shared join points, illustrated by aspects that represent idioms: Parameter checking and Error propagation. We present an approach for the detection of behavioral conflicts that is based on a novel abstraction model for representing the behavior of advice. The approach employs a set of conflict detection rules which can be used to detect both generic conflicts as well as domain or application specific conflicts. One of the benefits of the approach is that it neither requires the application programmers to deal with the conflict models, nor does it require a background in formal methods for the aspect programmers

FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure

Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection

Many important forms of data are stored digitally in XML format. Errors can occur in the textual content of the data in the fields of the XML. Fixing these errors manually is time-consuming and expensive, especially for large amounts of data. There is increasing interest in the research, development, and use of automated techniques for assisting with data cleaning. Electronic dictionaries are an important form of data frequently stored in XML format that frequently have errors introduced through a mixture of manual typographical entry errors and optical character recognition errors. In this paper we describe methods for flagging statistical anomalies as likely errors in electronic dictionaries stored in XML format. We describe six systems based on different sources of information. The systems detect errors using various signals in the data including uncommon characters, text length, character-based language models, word-based language models, tied-field length ratios, and tied-field transliteration models. Four of the systems detect errors based on expectations automatically inferred from content within elements of a single field type. We call these single-field systems. Two of the systems detect errors based on correspondence expectations automatically inferred from content within elements of multiple related field types. We call these tied-field systems. For each system, we provide an intuitive analysis of the type of error that it is successful at detecting. Finally, we describe two larger-scale evaluations using crowdsourcing with Amazon's Mechanical Turk platform and using the annotations of a domain expert. The evaluations consistently show that the systems are useful for improving the efficiency with which errors in XML electronic dictionaries can be detected.Comment: 8 pages, 4 figures, 5 tables; published in Proceedings of the 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, pages 79-86, February 201

Parameter analysis of copper-nickel-tungsten prepared via powder metallurgy process for electrical discharge machining of polycrystalline diamond

Polycrystalline Diamond (PCD) tools have an outstanding wear resistance. The electric conductivity of PCD caused by the conductive binding material (Cobalt) makes it possible to machine PCD tools with EDM. Electrode used in EDM of PCD must have better porosity, electrical and thermal conductivity. Therefore, this research presents the works in production of Cu-Ni-W electrode by powder metallurgy route. Production of powder metallurgy parts involve mixing of the powder with additives or lubricants, compacting the mixture and heating the green compacts in an Argon gas furnace so the particle bond to each other. Two levels of full factorial with six centre points and two replication technique was used to study the influence of main and interaction effects of the powder metallurgy parameter. There were four factors involved in this experiment. Factor A which is Type of Cu-Ni; Type A and Type B was defined as categorical factor. Factor B in which Composition of W; 5 Wt.%, 15 Wt. % and 25 Wt.%, was defined as numerical factor. Factor C which is the Compaction load; 7, 8 and 9 tonne and Factor D which is Sintering temperature; 635 ℃, 685 ℃ and 735 ℃ were also defined as numerical factor. Optical Microscope, Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) was used to analysed the microstructure and surface morphology of Cu-Ni-W electrode. The best parameter combination to produced better porosity, electrical and thermal conductivity for both Type A and Type B was 5 Wt.% of W, compaction load at 9 tonne and sintering temperature at 735℃. The best response for Type A is 12.65% of porosity, 14.40 IACS% of electrical conductivity and 413.26 W/m.℃ of thermal conductivity. While that, the best response for Type B were 9.36% of porosity, 16.66 IACS% of electrical conductivity and 345.21W/m.℃ of thermal conductivity. From the calculation of Maxwell’s Equation, Type A and Type B had the highest electrical conductivity of 58.48 IACS% and 77.35 IACS% respectively at W content of 5Wt.%. Type A and Type B also had the highest thermal conductivity of 369.86 W/m.℃ and 310.24 W/m.℃ respectively at W content of 5 Wt.%. Besides that, thermal conductivity also increased with the temperature increased until 450℃

Fluid-borne Particle Analysers

This invention describes an improved method and apparatus for the analysis of fluid borne particles and which is especially suitable for the detection of airborne biological particles. In one aspect of the invention provides an apparatus for the detection of fluid borne particles which comprises a zone through which a fluid to be analyzed flows in use, a source of illumination to illuminate/irradiate fluid borne particles present in said zone, and a detector to detect light from the particles as an indicator of the presence or characteristics of the particles, wherein the apparatus comprises an integrating sphere and the zone is within the integrating sphere. The apparatus is highly sensitive and can be used for detecting airborne particles even where the particles are present at very low particle concentrations in the air

Automated Development of Semantic Data Models Using Scientific Publications

The traditional methods for analyzing information in digital documents have evolved with the ever-increasing volume of data. Some challenges in analyzing scientific publications include the lack of a unified vocabulary and a defined context, different standards and formats in presenting information, various types of data, and diverse areas of knowledge. These challenges hinder detecting, understanding, comparing, sharing, and querying information rapidly. I design a dynamic conceptual data model with common elements in publications from any domain, such as context, metadata, and tables. To enhance the models, I use related definitions contained in ontologies and the Internet. Therefore, this dissertation generates semantically-enriched data models from digital publications based on the Semantic Web principles, which allow people and computers to work cooperatively. Finally, this work uses a vocabulary and ontologies to generate a structured characterization and organize the data models. This organization allows integration, sharing, management, and comparing and contrasting information from publications