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Molecular genetic analysis of individuals from 6 Egyptian and 33 German families with fragile X syndrome and 240 further patients with mental retardation was performed applying a completely non-radioactive system. The aim of our study was the development of a non-radioactive detection method and its implementation in molecular diagnosis of the fragile X syndrome. Furthermore, we wanted to assess differences in the mutation sizes between Egyptian and German patients and between Egyptian and German carriers of a premutation. Using non-radioactive polymerase chain reaction (PCR), agarose gel electrophoresis and blotting of the PCR products, followed by hybridisation with a digoxigenin-labelled oligonucleotide probe (CGG)5 and chemiluminescent detection, we identified the fragile X full mutation (amplification of a CGG repeat in the FMR-1 gene ranging from several hundred to several thousand repeat units) in all patients. We observed no differences in the length of the CGG repeat between the Egyptian and German patients and carriers, respectively. However, in one prenatal diagnosis, we detected only one normal sized allele in a female fetus using the PCR-agarose assay, whereas Southern blot analysis with the digoxigenin labelled probe StB 12.3 revealed presence of a full mutation. Our newly established nonradioactive genomic blotting method is based on the conventional radioactive Southern blot analysis. Labelling of the probe StB 12.3 with digoxigenin via PCR allowed the detection of normal, premutated and fully mutated alleles. For exact sizing of small premutated or large normal alleles, we separated digoxigenin labelled PCR products through denaturing polyacrylamide gelelectrophoresis (PAGE) and transfered them to a nylon membrane using a gel dryer. The blotted PCR-fragments can easily be detected with alkaline phosphate-labelled anti-digoxigenin antibody. The number of trinucleotide repeat units can be determined by scoring the detected bands against a digoxigenated M13 sequencing ladder. Our newly developed digoxigenin/chemiluminescence approach using PCR and Southern blot analysis provides reliable results for routine detection of full fragile X mutations and premutations.

This paper presents a different approach to tackle the Sound Source Localization (SSL) problem apply on a compact microphone array that can be mounted on top of a small moving robot in an indoor environment. Sound source localization approaches can be categorized into the three main categories; Time Difference of Arrival (TDOA), high-resolution subspace-based methods, and steered beamformer-based methods. Each method has its limitations according to the search or application requirements. Steered beamformer-based method will be used in this paper because it has proven to be robust to ambient noise and reverberation to a certain extent. The most successful and used algorithm of this method is the SRP-PHAT algorithm. The main limitation of SRP-PHAT algorithm is the computational burden resulting from the search process, this limitation comes from searching among all possible candidate locations in the searching space for the location that maximizes a certain function. The aim of this paper is to develop a computationally viable approach to find the coordinate location of a sound source with acceptable accuracy. The proposed approach comprises two stages: the first stage contracts the search space by estimating the Direction of Arrival (DoA) vector from the time difference of arrival with an addition of reasonable error coefficient around the vector to make sure that the sound source locates inside the estimated region, the second stage is to apply the SRP-PHAT algorithm to search only in this contracted region for the source location. The AV16.3 corpus was used to evaluate the proposed approach, extensive experiments have been carried out to verify the reliability of the approach. The results showed that the proposed approach was successful in obtaining good results compared to the conventional SRP-PHAT algorithm.

Using autonomous robot to detect chemical emissions and track plumes caused by fire, toxic gas leakage and explosive at their early stages, and swiftly localize their sources can avoid risking human health and potentially save lives. The benefits of deploying autonomous robot(s) rather than human beings in performing such hazardous tasks are obvious. Even though using real robots to research, develop, and experiment in real environment are normally preferred, modelling and simulation are indeed sometimes better options when such as a consistent and repeatable complex environment with controllable variables (i.e. wind velocity and plume propagation in this case) for experiments is important. This chapter presents one out of many possible modelling and simulation approaches for the research related to chemical plume tracking and source localization using robots, and covers the modelling of robot, the modelling of the environment, and the integration of both to become a platform.

There are an increasing number of passengers undertaking air travels on commercial airliners throughout the world annually. During the flights, passengers are possibly exposed to different contaminants such as bacterial and CO 2 from other passengers. As airliner cabins have high occupant density and flights can last from 1 to 20 hours, transport of contaminant could have serious impact on both passengers and aircraft crew. It is important to understand airflows and contaminant transport inside the aircraft cabin in order to reduce the negative impacts. Current aircraft cabin airflows can be analyzed by two different methods, experimental measurements and computer modeling. With the rapid increase in computer power, computer modeling is becoming more popular in study of aircraft cabin flows. Computational fluid dynamics (CFD) is the most used modeling approach since it is relatively inexpensive, fixable and able to obtain high level resolution results.The main scope of this research is to develop a frame work to simulate airflows and trace contaminant transport in an aircraft cabin using CFD. The predicted airflows and contaminant concentration are then used to train an Artificial Intelligent (AI) system. This trained AI system will be able to trace back the possible source of the contaminant once the transmission of contaminant happens in the aircraft cabin, e.g. the severe acute respiratory syndrome (SARS) transmission in a flight in Hong Kong in 2003.This paper reports the development of the CFD model of aircraft cabin flows and the transport of SARS in the cabin. In the project, the first milestone is to produce a section of an aircraft cabin of Airbus 320 using ANSYS/Design-Modeller. The cabin model includes half of the cabin with 7 rows of seats. The second milestone is to mesh the geometry using ANSYS/Meshing. The third milestone is to set up boundary conditions for both airflows and contaminant in ANSYS/CFX. The final objective is to solve the solutions in CFD and transfer the CFD results to an AI system developed by the authors. Some CFD predictions of the airflow patterns and contaminant transport in the cabin are reported in the paper. It is found that the flow in the cabin is quit complex. There is a weak longitudinal flow that plays a significant role in the spread of contaminant in the cabin. Some preliminary results of the AI system are also presented in the paper.