A rapid PCR protocol for marker assisted detection of heterozygotes in segregating generations involving 1BL/1RS translocation and normal wheat lines

A rapid and reliable polymerase chain reaction (PCR)-based protocol was developed for detecting zygosity of the 1BL/1RS translocation in hexaploid wheat. The protocol involved a multiplex PCR with 2 pairs of oligonucleotide primers, rye-specific Ris-1 primers, and consensus 5S intergenic spacer (IGS) primers, and digestion of the PCR products with the restriction enzyme, MseI. A small piece of alkali-treated intact leaf tissue is used as a template for the PCR, thereby eliminating the necessity for DNA extraction. The test is simple, highly sensitive, and rapid compared with the other detection systems of 1BS1RS heterozygotes in hexaploid wheat. PCR results were confirmed with AFLP analyses. Diagnostic tests for 1BL/1RS translocation based on Sec-1-specific ELISA, screening for chromosome arm 1RS controlled rust resistance locus Yr9, and the PCR test differed in their ability to detect heterozygotes. The PCR test and rust test detected more heterozygotes than the ELISA test. The PCR test is being used to facilitate S1 family recurrent selection in the Germplasm Enhancement Program of the Australian Northern Wheat Improvement Program. A combination of the PCR zygosity test with other markers currently being implemented in the breeding program makes this test economical for 1BL/1RS characterisation of S1 families.

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.

Detection of Babesia bigemina infection: use of a DNA probe - a review

The development of a repetitive DNA probe for Babesia bigemina was reviewed. The original plasmid (p(Bbi)16) contained an insert of B. bigemina DNA of approximately 6.3 kb. This probe has been evaluated for specificityand analytical sensitivity by dot hybridization with isolates from Mexico, the Caribbean region and Kenya. A partial restriction map has been constructed and insert fragments have been subcloned and utilized as specific DNA probes. A comparison of 32P labelled and non-radioactive DNA probes was presented. Non-radioctive detection systems that have been used include digoxigenin dUTP incorporation, and detection by colorimetric substrate methods. Derivatives from the original DNA probe have been utilized to detect B. bigemina infection in a) experimentally inoculated cattle, b) field exposed cattle, c) infected Boophilus microplus ticks, and d) the development of a PCR amplification system.

Detection of Trypanosoma cruzi DNA within murine cardiac tissue sections by in situ polymerase chain reaction

The use of in situ techniques to detect DNA and RNA sequences has proven to be an invaluable technique with paraffin-embedded tissue. Advances in non-radioactive detection systems have further made these procedures shorter and safer. We report the detection of Trypanosoma cruzi, the causative agent of Chagas disease, via indirect and direct in situ polymerace chain reaction within paraffin-embedded murine cardiac tissue sections. The presence of three T. cruzi specific DNA sequences were evaluated: a 122 base pair (bp) sequence localized within the minicircle network, a 188 bp satellite nuclear repetitive sequence and a 177 bp sequence that codes for a flagellar protein. In situ hybridization alone was sensitive enough to detect all three T. cruzi specific DNA sequences.

Plagiarism meets paraphrasing: insights for the new generation in automatic plagiarism detection

Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyse the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource which uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analysed in the light of this annotation. The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarising, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analysed, providing critical insights for the improvement of automatic plagiarism detection systems.

Object Detection in Images by Components

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.

Implantação de um sistema de detecção de vazamentos em oleodutos com tratamento de alarmes falsos

The pumping of fluids in pipelines is the most economic and safe form of transporting fluids. That explains why in Europe there was in 1999 about 30.000 Km [7] of pipelines of several diameters, transporting millíons of cubic meters of crude oil end refined products, belonging to COCAWE (assaciation of companies of petroleum of Europe for health, environment and safety, that joint several petroleum companies). In Brazil they are about 18.000 Km of pipelines transporting millions of cubic meters of liquids and gases. In 1999, nine accidents were registered to COCAWE. Among those accidents one brought a fatal victim. The oil loss was of 171 m3, equivalent to O,2 parts per million of the total of the transported volume. Same considering the facts mentioned the costs involved in ao accident can be high. An accident of great proportions can bríng loss of human lives, severe environmental darnages, loss of drained product, loss . for dismissed profit and damages to the image of the company high recovery cost. In consonance with that and in some cases for legal demands, the companies are, more and more, investing in systems of Leak detection in pipelines based on computer algorithm that operate in real time, seeking wíth that to minimize still more the drained volumes. This decreases the impacts at the environment and the costs. In general way, all the systems based on softWare present some type of false alarm. In general a commitment exists betWeen the sensibílity of the system and the number of false alarms. This work has as objective make a review of thé existent methods and to concentrate in the analysis of a specific system, that is, the system based on hydraulic noise, Pressure Point Analyzis (PPA). We will show which are the most important aspects that must be considered in the implementation of a Leak Detection System (LDS), from the initial phase of the analysis of risks passing by the project bases, design, choice of the necessary field instrumentation to several LDS, implementation and tests. We Will make na analysis of events (noises) originating from the flow system that can be generator of false alarms and we will present a computer algorithm that restricts those noises automatically

An Optimum-Path Forest framework for intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Sistema de aquisição de dados para estudos de transiente de pressão e detecção de vazamentos em oleodutos

In February 2011, the National Agency of Petroleum, Natural Gas and Biofuels (ANP) has published a new Technical Rules for Handling Land Pipeline Petroleum and Natural Gas Derivatives (RTDT). Among other things, the RTDT made compulsory the use of monitoring systems and leak detection in all onshore pipelines in the country. This document provides a study on the method for detection of transient pressure. The study was conducted on a industrial duct 16" diameter and 9.8 km long. The pipeline is fully pressurized and carries a multiphase mixture of crude oil, water and natural gas. For the study, was built an infrastructure for data acquisition and validation of detection algorithms. The system was designed with SCADA architecture. Piezoresistive sensors were installed at the ends of the duct and Digital Signal Processors (DSPs) were used for sampling, storage and processing of data. The study was based on simulations of leaks through valves and search for patterns that characterize the occurrence of such phenomena

An attack signature model to computer security intrusion detection

Internal and external computer network attacks or security threats occur according to standards and follow a set of subsequent steps, allowing to establish profiles or patterns. This well-known behavior is the basis of signature analysis intrusion detection systems. This work presents a new attack signature model to be applied on network-based intrusion detection systems engines. The AISF (ACME! Intrusion Signature Format) model is built upon XML technology and works on intrusion signatures handling and analysis, from storage to manipulation. Using this new model, the process of storing and analyzing information about intrusion signatures for further use by an IDS become a less difficult and standardized process.

Intrusion detection system using optimum-path forest

Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. Neural networks and Support Vector Machines have been also extensively applied to this task. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In this research, we introduce a new pattern classifier named Optimum-Path Forest (OPF) to this task, which has demonstrated to be similar to the state-of-the-art pattern recognition techniques, but extremely more efficient for training patterns. Experiments on public datasets showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, as well as allow the algorithm to learn new attacks faster than the other techniques. © 2011 IEEE.

Performance evaluation of the fuzzy ARTMAP for network intrusion detection

Recently, considerable research work have been conducted towards finding fast and accurate pattern classifiers for training Intrusion Detection Systems (IDSs). This paper proposes using the so called Fuzzy ARTMAT classifier to detect intrusions in computer network. Our investigation shows, through simulations, how efficient such a classifier can be when used as the learning mechanism of a typical IDS. The promising evaluation results in terms of both detection accuracy and training duration indicate that the Fuzzy ARTMAP is indeed viable for this sort of application.

Wavespeed measurement in buried water distribution pipes and its significance in leak location

To determine the location of leaks in buried water pipes, acoustic methods are often used. These have proven to be very effective in metallic pipes but have been problematic in modern plastic pipes. In this paper the reason why this is so is discussed together with some measurements that were made on a bespoke test rig built by South Staffs Water plc. A particular problem is the estimate of the wavespeed. Tables are frequently used for this purpose, but these are often inaccurate and this means that a leak cannot be located accurately. An in-situ measure of the wavespeed is thus preferable. In this paper it is shown that there are significant issues in obtaining an accurate estimate of the wavespeed when a leak is present in the system. A method is proposed that overcomes some of these problems, which is discussed and is demonstrated using some data from the bespoke test-rig. © (2013) Trans Tech Publications.

Detection of individual spin transitions of a single proton confined in a cryogenic Penning trap

Das in dieser Arbeit vorgestellte Experiment zur Messung des magnetischen Moments des Protons basiert auf der Messung des Verhältnisses von Zyklotronfrequenz und Larmorfrequenz eines einzelnen, in einer kryogenen Doppel-Penning Falle gespeicherten Protons. In dieser Arbeit konnten erstmalig zwei der drei Bewegungsfrequenzen des Protons gleichzeitig im thermischen Gleichgewicht mit entsprechenden hochsensitiven Nachweissystemen nicht-destruktiv detektiert werden, wodurch die Messzeit zur Bestimmung der Zyklotronfrequenz halbiert werden konnte. Ferner wurden im Rahmen dieser Arbeit erstmalig einzelne Spin-Übergänge eines einzelnen Protons detektiert, wodurch die Bestimmung der Larmorfrequenz ermöglicht wird. Mithilfe des kontinuierlichen Stern-Gerlach Effekts wird durch eine sogenannte magnetische Flasche das magnetische Moment an die axiale Bewegungsmode des Protons gekoppelt. Eine Änderung des Spinzustands verursacht folglich einen Frequenzsprung der axialen Bewegungsfrequenz, welche nicht-destruktiv gemessen werden kann. Erschwert wird die Detektion des Spinzustands dadurch, dass die axiale Frequenz nicht nur vom Spinmoment, sondern auch vom Bahnmoment abhängt. Die große experimentelle Herausforderung besteht also in der Verhinderung von Energieschwankungen in den radialen Bewegungsmoden, um die Detektierbarkeit von Spin-Übergängen zu gewährleisten. Durch systematische Studien zur Stabilität der axialen Frequenz sowie einer kompletten Überarbeitung des experimentellen Aufbaus, konnte dieses Ziel erreicht werden. Erstmalig kann der Spinzustand eines einzelnen Protons mit hoher Zuverlässigkeit bestimmt werden. Somit stellt diese Arbeit einen entscheidenden Schritt auf dem Weg zu einer hochpräzisen Messung des magnetischen Moments des Protons dar.

An ecological approach to anomaly detection: the EIA Model.

The presented work proposes a new approach for anomaly detection. This approach is based on changes in a population of evolving agents under stress. If conditions are appropriate, changes in the population (modeled by the bioindicators) are representative of the alterations to the environment. This approach, based on an ecological view, improves functionally traditional approaches to the detection of anomalies. To verify this assertion, experiments based on Network Intrussion Detection Systems are presented. The results are compared with the behaviour of other bioinspired approaches and machine learning techniques.